Biomedical materials (Bristol, England)Nikhil Anand, Kaushik Pal
The selection of biomaterial is crucial for the long-term success of implants. Materials that perform an adequate function and reduce negative biological responses should be taken. Due to their good mechanical strength, stainless steel, titanium, and Co-based alloys have been utilized for implant purposes; however, their permanent nature and very low corrosion rates may lead to long-term clinical complications. Researchers are looking for biomaterials that combine suitable mechanical properties with controlled and uniform degradation behaviour. In the last decade, magnesium and iron-based alloys have been seen as a good alternative and examined as promising biodegradable metals for implant applications. However, their excessively rapid corrosion (Mg) or extremely slow degradation (Fe) imposes significant limitations on their clinical applicability. In recent times, zinc-based alloys have been seen as new materials that will challenge magnesium and iron-based alloys. Zn2+ions released from zinc metal corrosion play a crucial role in bone metabolism, enzymatic activity, and cellular proliferation. However, the low mechanical strength and limited ductility of pure zinc restrict its direct utilization in load-bearing implants. Therefore, the fabrication of high-strength and ductile zinc-based alloys while maintaining biocompatibility and suitable corrosion rate remains a main research challenge. This article critically assesses and compares the mechanical properties, corrosion behaviour, and biocompatibility of magnesium-, zinc-, and titanium-based alloys, and inspects the impact of advanced fabrication methods, particularly additive manufacturing, on microstructure evolution and implant performance.
PloS oneUtku Murat Kalafat, Hüseyin Mutlu, Ramiz Yazıcı, Murat Genç, Bensu Bulut, Medine Akkan Öz, Ayşenur Gür, Mehmet Yortanlı, Uğur Şakar
BACKGROUND: Supracondylar humeral fractures constitute 10-16% of pediatric skeletal injuries, requiring timely diagnosis to prevent neurovascular complications. Developmental variations in pediatric bone structures pose diagnostic challenges for clinicians. This study evaluated three next-generation large language models (LLMs) (ChatGPT-4o, Gemini 2.0, Claude 3.5) for detecting pediatric supracondylar humeral fractures and their classification according to the Gartland system. METHODS: This retrospective observational study included 300 pediatric patients (150 with supracondylar humeral fractures confirmed by expert consensus, 150 without fractures) aged 2-10 years presenting to the Emergency Department of the Bilkent City Hospital (October 2022-January 2025). Two-view elbow radiographs were presented to each LLM three times on different days. Diagnostic accuracy was evaluated using overall accuracy (all three responses correct), strict accuracy (≥2 correct responses), and ideal accuracy (≥1 correct response). Response consistency was assessed using Fleiss' Kappa coefficient. Fractures were classified according to modified Gartland criteria. RESULTS: Gemini 2.0 demonstrated highest sensitivity (68.4%) followed by Claude 3.5 (58.7%) and ChatGPT-4o (19.3%) for fracture detection (p < 0.001). Ideal accuracy rates were 83.3%, 78.7%, and 27.3% respectively. Although ideal accuracy rates exceeded 91% in non-fracture cases, specificity remained low (33.1-36.0%), indicating a high rate of false-positive classifications. Response consistency was very good for ChatGPT-4o (κ = 0.69) and Gemini 2.0 (κ = 0.61), good for Claude 3.5 (κ = 0.44). For Gartland classification, Gemini 2.0 achieved highest accuracy: Type I (83.3%), Type II (62.4%), Type III (68.7%). CONCLUSION: Current LLMs demonstrate limited capability as independent diagnostic tools for pediatric supracondylar humeral fractures. Gemini 2.0's 68.4% sensitivity indicates these technologies require specialized pediatric training before clinical implementation. However, their potential as assistive tools for triage and assessment warrants further development of pediatric-specific models.
BMJ openJoanna Maggs, Hannah James, Julia Gauly, Helen Vint, Homa Arshad, Sarah McMahon, Arabella Scantlebury, Amy Grove
OBJECTIVES: The study aimed to explore why trauma and orthopaedic registrars decide to discontinue surgical training. Understanding the factors that influence a decision to leave may help to inform changes which enhance the experiences of surgeons and retention of the future workforce. DESIGN: Qualitative study using semi-structured interviews. SETTING: Between October 2022 and March 2026, interviews were conducted with participants who had left the UK National Health Service (NHS) trauma and orthopaedic specialty training before completion. Reflexive thematic analysis was performed on 1727 min of interview data. RESULTS: 23 respondents were interviewed. Most respondents exited specialty training in the middle years (ST5 and 6). 13 retrained in the NHS and 10 left the NHS entirely. Three overarching themes were identified: (1) 'The Hidden Curriculum' describes the presence of unwritten rules, attributes and behaviours which must be met to succeed in training (2) 'Flawless Care in a Flawed System' describes surgeons' responsibility for delivering faultless care while lacking agency to address system failures and (3) 'Interchangeable and Underappreciated' depicts the dissonance between trainees' professional status and their perceptions of being interchangeable. CONCLUSIONS: The loss of surgical registrars from training programmes comes at a high cost to the healthcare system and society as well as to the individual. Understanding what triggers surgeons to leave training allows consideration of how challenges can be modified to prevent further attrition and ensure sustainability of the profession.
JMIR medical educationAdam Tibor Schlegl, Marko Ostojić, Ines Unterfrauner, Gianluca Ciolli, Panayiotis D Megaloikonomos, Vasilios G Igoumenou, Michele Mercurio, Thomas Stark, David…
BACKGROUND: Digital technologies increasingly shape postgraduate medical education, yet orthopedic and trauma training face unique challenges because of the tactile, procedurally focused skills involved. Digital tools partially address these needs, but gaps remain, particularly across diverse European contexts. OBJECTIVE: Our primary aim was to quantitatively assess predictors of digital learning technology acceptance (e-learning, distance education, and virtual reality [VR] and augmented reality [AR]) among European orthopedic and trauma trainees, drawing on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) as conceptual guides. Specifically, we examined how perceived usefulness and perceived ease of use (TAM), alongside performance expectancy, effort expectancy, social influence, and facilitating conditions (UTAUT), related to trainees' acceptance of digital technologies. These constructs guided variable selection and grouping, attitudinal scale design, and interpretation of how individual and contextual factors shape acceptance of digital learning tools in orthopedic training. Secondary aims were to describe adoption and attitude patterns, identify attitudinal trainee profiles, and examine contextual associations (eg, workplace type and national gross domestic product [GDP]). METHODS: We distributed a multinational survey via European trainee federations and used validated scales to assess digital competence and attitudes and gathered demographic data (n=217 across 29 European countries). We administered the questionnaire in English; however, respondents who self-reported English proficiency below the intermediate level were excluded from the analyses to minimize potential comprehension-related bias. The survey assessed digital experience, self-reported digital competence, and attitudes toward e-learning, distance education, and VR/AR, and collected detailed demographic and workplace data. Expert review, cognitive pretesting, and pilot testing ensured validity and clarity. Analytical methods included Wilcoxon tests, ANOVA, clustering, multinomial logistic regression, and factor analysis to ensure the reliability and validity of attitudinal measures. RESULTS: e-Learning technologies were the most widely adopted, whereas VR/AR tools were less frequently used despite high average attitude ratings (mean 4.07, SD 0.88). Cluster analysis identified 3 distinctive groups-enthusiastic, supportive, and hesitant-that differed significantly in digital competence and acceptance profiles. Digital competence and national GDP emerged as significant predictors of group membership, consistent with TAM/UTAUT expectations that perceived capability and contextual facilitating conditions shape acceptance. Variation in attitudes was further associated with workplace type and regional resource disparities, underscoring the influence of contextual factors on technology adoption. CONCLUSIONS: European orthopedic trainees show broad support for digital innovations, preferring VR/AR despite low use. Preliminary evidence supports digital competence as a key mediator of acceptance, with GDP and workplace disparities predicting profiles (hesitant vs enthusiastic). Competence-first strategies and targeted resource equity (eg, low-GDP subsidies), together with policy adjustments, may address regional disparities. Future longitudinal and multimethod studies are needed to test causal pathways implied by TAM and UTAUT and to evaluate the generalizability of these findings across specialties and educational contexts.
Journal of the American Academy of Orthopaedic Surgeons. Global research & reviewsJoshua U Hancock, Jordan H Larson, Kaitlyn N Christmas, Raahil Patel, Christian M Schmidt, Caroline Chebli, Mark A Frankle
BACKGROUND: Total shoulder arthroplasty (TSA) has experienced rapid growth. Yet graduating orthopaedic surgery resident (GOSR) TSA volumes are not individually reported due to its exclusion from the Accreditation Council for Graduate Medical Education (ACGME) case minimum list. Rather, TSAs are grouped within the shoulder repair/revision/reconstruction (RRR) category. This study evaluated long-term trends and identified disparities in GOSR shoulder case volumes since the inception of case minimums in 2013. METHODS: Publicly available ACGME case logs of 8247 GOSRs were analyzed from 2014 to 2024. Average case volumes were compared between 2014 and 2024. Trends in case volume differences between the 10th and 90th percentile GOSRs were assessed. Changes in the proportions of each procedure category relative to overall shoulder procedures were analyzed. RESULTS: GOSRs performed more shoulder RRR and overall shoulder cases in 2024 than in 2014 (RRR: 46.4 vs. 24.2 cases [+91.7%]; overall: 174.9 vs. 125.9 cases [+38.9%]; P < 0.001). Differences in shoulder RRR and overall shoulder procedures between the 10th and 90th percentiles increased year-over-year (+2.86 and +5.11 cases/year, respectively; P < 0.001), with the gap between percentile groups demonstrating consistent expansion (RRR: ρ = 0.975; overall: ρ = 0.961; P < 0.001). Relative to overall shoulder procedures, the proportion of shoulder RRRs increased from 2014 to 2024 (19.2% to 26.5%, P = 0.045). CONCLUSION: Although average GOSR shoulder case volumes increased over the long term, interresident disparities widened. Shoulder RRRs expanded to more than one fourth of resident shoulder procedures, paralleling TSA growth nationally. Residency governance bodies should consider implementing a resident TSA case minimum to ensure experience in this important procedure.
Archives of orthopaedic and trauma surgerySuzen Agharia, Shayan Soroush, Daniel Ameen, Yushy Zhou
INTRODUCTION: This descriptive study aimed to longitudinally evaluate the performance of contemporary large language models - ChatGPT-5, Gemini 2.5 Flash, and Grok-3 - on orthopaedic clinical multiple-choice tasks, benchmarked against pooled clinician consensus. A secondary aim was to assess whether recent advances in generative AI translated into improved alignment with clinician consensus compared with previous AI models. MATERIALS AND METHODS: A total of 97 multiple-choice clinical cases spanning eight orthopaedic subspecialties were sourced from OrthoBullets and previously benchmarked against aggregated responses from thousands of practising clinicians. Using identical methodology to our 2023 study of ChatGPT-3.5, ChatGPT-4, and Bard, each model was prompted with standardised case stems and response options. The primary outcome was the proportion of AI responses matching the most popular clinician response; secondary analyses assessed agreement within 10% and 20% of clinician consensus, performance on 'controversial' (< 25% margin) questions, and inter-model concordance using Cohen's kappa coefficients. RESULTS: Gemini 2.5 Flash achieved the highest alignment with clinician consensus (69.1%), followed by Grok-3 (66.0%) and ChatGPT-5 (58.8%). None of the LLMs refused to respond to any prompts, representing a reduction from 7.2% from our 2023 study. Subspecialty analysis demonstrated that Gemini 2.5 Flash performed best in Hand and Paediatric domains, while Grok-3 excelled in Reconstruction, Trauma, and 'controversial' cases. Inter-model agreement was highest between Grok-3 and Gemini 2.5 Flash (κ = 0.678), indicating improved consistency compared with prior-generation systems. CONCLUSIONS: Contemporary LLMs can be promising adjuncts for orthopaedic education by simulating peer reasoning and offering structured explanations in non-critical settings. Despite incremental gains in reasoning capability compared to previous AI models, contemporary LLMs remain unsuitable for independent clinical use. Future research should develop hybrid clinician-AI workflows and longitudinal benchmarks to distinguish true reasoning improvements from memorisation.
BMJ openIsabella Herrington, Julie Makarski, Divya Mathura, Cari M Whyne, Elias B Khalil, Bheeshma Ravi, Aazad Abbas, Johnathan R Lex, Monika Kastner
OBJECTIVES: Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room (OR) resources and affecting patient outcomes and healthcare spending. Manual scheduling systems in Ontario orthopaedic centres create wide variations in wait times, with recent declines in meeting benchmark targets despite increased procedure volumes. Challenges stem from fragmented referral processes, outdated scheduling methods and resource constraints. Artificial intelligence and machine learning (ML) offer potential solutions for optimising scheduling; however, their implementation remains inconsistent. This study aims to identify determinants affecting the rollout of a new ML-driven automated scheduling system at a high-volume elective orthopaedic surgery centre. DESIGN: A qualitative description approach supported by implementation science frameworks. SETTING: A high-volume elective orthopaedic surgery unit at a Canadian tertiary care centre. PARTICIPANTS: 17 individuals from clinical, administrative and leadership roles who were directly involved in surgical scheduling. INTERVENTIONS: A new ML-driven automated surgical scheduling system. OUTCOMES: Perceptions of the proposed new surgical scheduling system (barriers and enablers of implementation, recommendations for improvement). RESULTS: Three main themes were identified, capturing challenges and enablers in the existing scheduling system: system functionality, process-related factors and resource constraints.Participants described substantial inefficiencies in the existing manual scheduling system, including outdated software, fragmented information systems, inconsistent communication and resource constraints. Across interest-holder groups, there was broad but variable perceived support for a planned ML-enabled scheduling system, particularly for improving duration prediction, access to scheduling data and reporting, alongside concerns about system complexity, workflow fit, training and resource implications. Interest-holders emphasised the importance of user-friendly design, interoperability, responsive training, phased implementation and ongoing feedback. CONCLUSIONS: This pre-implementation qualitative study identified significant process and resource limitations in manual orthopaedic surgical scheduling, but interest-holder support for a well-designed ML-driven system is strong. While participants anticipated potential benefits for scheduling accuracy, throughput and resource allocation, these perceived advantages will require meaningful user engagement, robust training, phased rollout and evaluation in subsequent implementation and outcome studies.
Medical science monitor : international medical journal of experimental and clinical researchGai Zhang, Fang Shu Li, Mei Lin, Juan Shao Huang
BACKGROUND Chronic refractory wounds in orthopedic patients not only impede physical recovery but also cause sleep disturbances due to treatment-related anxiety. This study aims to investigate the effects of combining clay pot therapy with thunder-fire moxibustion on the wound healing and sleep quality of patients with chronic refractory wounds. MATERIAL AND METHODS From June 2022 to December 2024, 60 patients in the department of orthopedics of a class III hospital in Guangdong Province were randomly divided into an observation group (n=30) and control group (n=30). The control group received conventional treatment, including anti-infective therapy for chronic wounds, debridement, measures to improve microcirculation, and neurotrophic support. The observation group received clay pot therapy combined with thunder-fire moxibustion in addition to the treatments administered to the control group. Wound healing rate, local symptom scores, pain scores, and Pittsburgh Sleep Quality Index scores were compared between the 2 groups. RESULTS After the intervention, the observation group achieved a significantly higher cure rate (26.67% vs 3.33%), lower local symptom scores (5.08±1.60 vs 7.02±1.89), lower pain scores (1.89±2.06 vs 2.97±1.98), and better Pittsburgh Sleep Quality Index scores (9.58±1.56 vs 11.30±2.15), compared with the control group (all P<0.05). CONCLUSIONS Clay pot combined with thunder-fire moxibustion in the treatment of chronic refractory wounds in the orthopedics department can promote wound healing, improve sleep quality, and improve the clinical curative effect.
Southern medical journalFahad Nadeem, Saad Ibrahim, Sean Taylor, Saurabh Rawall, Zuhair Mohammad, Humza Pirzadah, José Ayala-Ortiz, Sakthivel Rajaram
OBJECTIVES: Large language models (LLMs), such as Open AI's Chat Generative Pre-Trained Transformer (GPT)-4 and Google Gemini, have gained significant attention for their ability to process complex language patterns and are being used increasingly in fields such as medicine, where they assist in learning, collaboration, and patient care. Although prior studies have evaluated LLMs on medical licensing examinations, limited research compares their performance on orthopedic-specific assessments. This study aims to assess the accuracy and response speed of ChatGPT-3.5, ChatGPT-4, Microsoft Copilot, and Gemini on the Orthopedic In-Training Examination (OITE). METHODS: Questions from the 2020-2022 OITE were extracted from the American Academy of Orthopaedic Surgeons' question bank. Each question, along with four answer choices, was manually input into the LLMs without response prompts or feedback. Response accuracy and speed were recorded, with timing measured from the moment the question was submitted until an answer was generated. RESULTS: Out of 1582 prompts, ChatGPT-4 demonstrated the highest accuracy (67.09%±0.08%), significantly outperforming ChatGPT-3.5, Microsoft Copilot, and Gemini (P<0.001). ChatGPT-3.5 was the fastest, with an average response time of 5.41±0.10 seconds. Both ChatGPT-3.5 and ChatGPT-4 responded significantly faster than Gemini and Microsoft Copilot (P<0.001). CONCLUSIONS: ChatGPT-4 exhibited the highest accuracy on OITE questions, and ChatGPT-3.5 was the fastest. Gemini and Copilot were generally less accurate in their responses and had a slower response time. These findings highlight the potential of LLMs in orthopedic education and emphasize the need for further research to explore their broader applications in medical training and decision making.
The Nigerian postgraduate medical journalLukman Olalekan Ajiboye, Abdullallahi Bello Galadima, Nuradeen Altine Aliyu, Maryam Muhammad Hussaini
BACKGROUND: Healthcare litigation is increasing globally and imposes significant emotional, financial and reputation and trust-related costs. Orthopaedic and spine surgery carry particularly high medico-legal risk because of technical complexity, uncertain outcomes and heightened patient expectations. Alternative dispute resolution (ADR), especially mediation, offers confidential and collaborative means to both prevent and resolve conflict. OBJECTIVES: To (i) examine litigation-prevention strategies and ADR modalities applicable to orthopaedic and spine practice; (ii) synthesise common sources of conflict, the advantages of ADR over litigation and implementation challenges within the Nigerian health system and (iii) propose recommendations for hospital governance, professional regulation and incorporation of ADR training in medical education. METHODOLOGY: A narrative review of English-language literature was conducted across biomedical databases and grey sources using predefined keywords ADR, ADR processes with outcome and barriers in low- and middle-income countries, and patient safety. Eligible studies included empirical research and programme reports. Thematic synthesis focused on conflict drivers, mediation, on medical litigation, orthopaedics, policy and legal analyses, preventive mechanisms and spine surgery. RESULTS: One hundred and fifty-two sources met the inclusion criteria. Major conflict drivers were consent and communication failures, poor documentation, expectation-outcome mismatch, disputes over surgical indication, peri-operative complications, implant and device issues, financial hardship and delayed presentation. ADR consistently showed advantages in cost, timeliness, confidentiality, therapeutic relationship preservation and learning potential. Barriers included weak institutional ADR frameworks, limited mediator clinical literacy, fragmented regulation, low insurance penetration and poor quality-improvement data. CONCLUSIONS: ADR provides an effective, confidential approach to reducing litigation in orthopaedic and spine practice by addressing communication failures early and preserving therapeutic relationships. Strengthening consent, documentation, institutional ADR pathways and mediator clinical skills, supported by policy reforms and ADR education, can reduce preventable conflicts and promote safer, more accountable surgical care in Nigeria.
JBJS reviewsMads Emil Jacobsen, Rasmus Drews Wenda, Mikkel Engell Sandager Nielsen, Kristoffer Borbjerg Hare, Lars Konge, Amandus Gustafsson
BACKGROUND: Immersive virtual reality (iVR) is increasingly used in orthopedic surgical training, and many platforms generate automated performance metrics that enable scalable competency-based training programs. However, the extent to which iVR-based assessments are supported by validity evidence remains unclear. The purpose of this study was to conduct a scoping review of iVR-based assessments in orthopedic surgery and map validity evidence using Messick's framework. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews guidelines, we searched PubMed and Embase for original studies evaluating technical performance within orthopedic iVR simulations. Two reviewers independently screened studies and extracted data. They appraised assessment validity evidence mapped to Messick's framework using the Ghaderi scoring instrument and evaluated methodological quality using the Medical Education Research Study Quality Instrument (MERSQI). Data were synthesized descriptively. RESULTS: Eighteen studies spanning 11 orthopedic procedures met the inclusion criteria. Validity evidence supporting score interpretation was limited across all 5 of Messick's domains: Total Ghaderi scores ranged from 0 to 7 (median 2.5) of 15, with 0 being the most frequent domain-level rating. Relations to other variables was the most addressed domain, while internal structure and consequences evidence were largely absent. By contrast, methodological quality was generally high (median MERSQI 13.5 of 18), but reporting quality was inconsistent. CONCLUSION: Despite the growing use of iVR in orthopedic training, current iVR-based assessments lack sufficient validity evidence to support meaningful interpretation of performance scores. The field risks mistaking technological sophistication for assessment quality. Strengthening iVR-based assessment requires clearer construct definitions, systematic validity evidence across Messick's domains, and improved reporting practices. LEVEL OF EVIDENCE: Level III. See Instructions for Authors for a complete description of levels of evidence.
JMIR medical educationChengcheng Yu, Fangcai Li, Ning Zhang, Hejia Hu, Hanxu Huang, Jingkai Wang, Yiqing Tao, Yinan Wu
BACKGROUND: Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education. OBJECTIVE: This study aims to evaluate the effectiveness of AI-assisted peer teaching compared to traditional peer teaching in orthopedic clinical education, with respect to knowledge acquisition, clinical skills development (particularly clinical reasoning), student engagement, and 3-month knowledge retention. METHODS: This historical cohort study compared 2 consecutive cohorts of medical students (aged 20-27 years, 108/190, 56.8% male) at the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. All eligible students from each cohort were enrolled. The control group (2021 cohort, n=96, taught in 2024) received traditional peer teaching; the intervention group (2022 cohort, n=94, taught in 2025) received AI-assisted peer teaching with access to DeepSeek-V3. Primary outcomes were assessed using a validated 50-item multiple-choice examination (0-100 points) and a 4-station Objective Structured Clinical Examination (OSCE; 0-100 points) with standardized rubrics (intraclass correlation coefficient>0.85). Secondary outcomes included student engagement and satisfaction (5-point Likert scales) and AI usage metrics. Assessments were conducted at baseline, postintervention (8 weeks), and 3-month follow-up. Analysis of covariance adjusted for baseline knowledge, prior AI experience, and learning interest to address observed baseline imbalances. RESULTS: Using independent samples t tests (α=.05, 2-tailed), the AI-assisted group demonstrated significantly higher postintervention knowledge scores (mean 79.69, SD 8.41 vs mean 75.33, SD 9.26; mean difference=4.36, 95% CI 1.84-6.87; P<.001; Cohen d=0.49). OSCE total scores were significantly higher (mean 80.95, SD 7.57 vs mean 76.24, SD 9.23; mean difference=4.71, 95% CI 2.31-7.11; P<.001; d=0.56), with clinical reasoning showing the largest effect (mean difference=2.22, 95% CI 1.18-3.25; P<.001; d=0.61). Analysis of covariance adjusted results remained significant for all primary outcomes (adjusted knowledge difference=3.52, P=.002; adjusted OSCE difference=4.52, P<.001). At 3-month follow-up (174/190, 91.6%), the AI-assisted group maintained higher knowledge scores (mean 77.36, SD 8.60 vs mean 72.84, SD 10.42; mean difference=4.52, 95% CI 1.68-7.36; P=.002; d=0.47), with similar knowledge decay rates between groups. CONCLUSIONS: This study provides the first systematic evidence that integrating AI tools within structured peer teaching enhances orthopedic clinical education across multiple domains, including knowledge acquisition, OSCE performance, and student engagement. Unlike prior studies examining AI as a stand-alone learning tool, this work demonstrates the synergistic potential of combining AI knowledge support with peer teaching's social learning benefits, with particularly strong effects on clinical reasoning. These findings support scalable, cost-effective implementation of AI-augmented peer teaching, though randomized controlled trials are needed to confirm causality and determine optimal implementation strategies.
Journal of the American Academy of Orthopaedic Surgeons. Global research & reviewsRodnell Busigó Torres, Charu Jain, Luca M Valdivia, Brocha Z Stern, Brett L Hayden
BACKGROUND: Diversity in the orthopaedic surgery workforce is essential for addressing healthcare disparities, enhancing patient-physician trust, and improving outcomes. Despite a growing diverse US population, orthopaedics remains one of the least diverse surgical specialties. However, diversity within musculoskeletal oncology fellowships has not been examined. Identifying disparities in representation is a critical step toward fostering a more inclusive workforce that better reflects the patient population. This study asked (1) is female representation equitable in musculoskeletal oncology fellowships, and (2) are individuals from underrepresented racial and ethnic backgrounds equally represented? METHODS: We analyzed publicly available demographic data for US medical graduates, orthopaedic surgery residents, and musculoskeletal oncology fellows from 2013 to 2022. Sex and race/ethnicity were extracted, and annual percentages for each demographic category were assessed. Participation-to-prevalence ratios (PPRs) were calculated to determine overrepresentation (>1.2), equitable representation (0.8 to 1.2), and underrepresentation (<0.8) relative to US Census data. RESULTS: Female fellows were underrepresented (PPR = 0.73). Hispanic and Black fellows were also underrepresented, with PPRs of 0.21 and 0.29, respectively. Notably, there were no Native Hawaiian/Pacific Islander or Native American/Alaskan Native fellows in this subspecialty. Asian fellows were overrepresented (PPR = 1.47), and White fellows were equitably represented (PPR = 1.08). CONCLUSION: Musculoskeletal oncology fellowships continue to demonstrate sex and racial/ethnic disparities, with notable underrepresentation of female, Hispanic, Black, Native Hawaiian/Pacific Islander, and Native American/Alaskan Native fellows. Despite increasing diversity at earlier stages of medical training, these improvements are not reflected in fellowship representation, highlighting the need for targeted efforts to promote inclusivity.
InjuryJeandre Deon Kotze, Mikail Amod, Cornel Breedt, Cameron Anley, Nando Ferreira, Dane Maimin, Marc Nortje
BACKGROUND: Operative orthopaedic care in resource-constrained systems is frequently limited by theatre time, staffing, peri-operative support, implants, instruments and bed availability. In the absence of an explicit prioritisation framework, decisions about patients awaiting surgery may vary between clinicians and institutions. This modified Delphi study aimed to establish consensus on factors that should structure prioritisation of operative orthopaedic care in South African public hospitals. METHODS: A three-round modified Delphi study was conducted among South African public-sector orthopaedic clinicians. Round 1 used open-ended responses to generate candidate prioritisation factors. Round 2 used a structured 1-9 importance scale to rate patient-specific factors, injury-specific factors, red-flag conditions and potential tie-breakers. Round 3 verified operational definitions, anchor levels and red-flag handling. The scope was confined a priori to admitted patients with stable, isolated orthopaedic injuries; polytrauma, spinal cord injuries and unstable vertebral fractures were excluded because they require individualised, time-critical prioritisation through established emergency pathways. Inclusion consensus was defined as a median score of at least 4 with at least 75% of respondents rating the factor 4-9. Strong inclusion consensus required a median score of at least 7 with at least 75% rating the factor 7-9. Binary consensus required at least 75% agreement. RESULTS: Following exclusion, 65, 55 and 63 responses were analysed in Rounds 1, 2 and 3, respectively. Six patient-specific factors reached inclusion consensus: age, diabetes mellitus, severe cardiac or respiratory disease, premorbid functional status, physiological reserve and current psychosis or severe psychiatric instability. All six injury-specific factors reached inclusion consensus. Soft-tissue status, anatomical site and neurovascular status reached strong inclusion consensus; fracture type or pattern, injury energy and time already waited for surgery reached inclusion consensus. Acute compartment syndrome and threatened vascular status of the relevant limb reached consensus as automatic red-flag overrides. No proposed tie-breaker reached consensus. CONCLUSIONS: This study provides a consensus framework for factors influencing the prioritisation of patients awaiting operative orthopaedic care in a low- and middle-income country (LMIC) setting. The findings support a preliminary framework, rather than a validated scoring system. Prospective weighting, inter-rater reliability testing and outcome validation are required before implementation as a formal prioritisation tool.
Journal of evaluation in clinical practiceMeghan M JaKa, Maren S G Henderson, Jennifer M Dinh, Megan Reams, Bradly Nelson, Steven P Dehmer, Jeanette Y Ziegenfuss
RATIONALE: Many advances have been made to identify novel, effective orthopedic care practices. For all that is known about the comparative effectiveness of various clinical decisions in anterior cruciate ligament (ACL) injury care, surprisingly little is known about how surgeons approach decision-making in the face of this evidence. Implementation science models such as Capability, Opportunity, Motivation - Behavior (COM-B) offer a way to organize and comprehensively understand how clinicians make decisions. AIMS AND OBJECTIVES: This evaluation aimed to understand and quantify the factors that influence orthopedic surgeons' decision-making in ACL injury care according to COM-B. METHODS: This pragmatic evaluation used a sequential exploratory mixed-methods approach combining orthopedic leader discussions and qualitative semi-structured interviews, followed by four rounds of quantitative census surveys to understand the factors influencing four surgeon decision points related to ACL injury care. First, two authors (BN a practicing surgeon with a clinical and research leadership role and MR the director of the affiliated orthopedic research institute) participated in recurring in-person orthopedic leader discussions; then additional selected surgeons were invited to participate in one-time qualitative semi-structured phone interviews; and, finally all surgeons that perform ACL procedures in the health system were invited to participate in four quantitative web surveys. Results were summarized descriptively according to the COM-B model. RESULTS: Two co-authors (BN and MR) participated in the orthopedic leader discussions, five invited surgeons participated in qualitative interviews (100% response rate), and 10-11 surgeons participated in four survey rounds (83%-100% response rate). Factors influencing each of the four selected decision points were identified within each COM-B category. Clinical knowledge (capability) and social influence of patient preferences (opportunity) were identified as highly influential for all decisions. Automatic decision making or habit (motivation) was also highly important for most but not all decisions. CONCLUSIONS: Interviews and surveys with surgeons from one Midwest U.S. health system demonstrate the complexity of ACL injury care decision-making. Surgeons reported that capability, opportunity, and motivation were important in all four decisions. The most important factors influencing decisions ranged from their technical ability to perform one surgical technique over another (e.g., hamstring autograft) in selecting what type of surgical graft to use to their patients' preferences for having surgery in deciding whether to recommending surgery. Future research to identify and test intervention strategies like shared decision-making training in alignment with the identified factors like technical ability and social influences have the potential to lead to higher-value orthopedic care by supporting decisions like recommending longer pre-operative rehabilitation.
BiosensorsGiorgia Polidori, Andrea Visani, Gianluca Giavaresi, Mauro Serpelloni, Gregorio Marchiori
Sensors are an established driver of diagnostics and prevention in the medical field, including orthopaedics. Today, the subclass of ion-selective sensors (ISSs) is on the leading edge due to its advantages, enabled by technological advancements in manufacturing, such as miniaturization, precision, accuracy, specificity, a wide measuring scale, ease of use, flexible operating conditions, and measuring speed. While ISSs' impact on environmental and health fields is already the subject of investigation, it still needs to be analysed specifically in orthopaedics, which is the aim of this Review. A PubMed and Scopus search was performed using the keywords "ion", "sensor", "electrodes", "selective", "musculoskeletal", "implant", "joint replacement", and "orthopaedic"; after systematic screening, 44 studies were included in the synthesis. First, studies were classified based on the target ion. Only a few papers treated applications specifically in orthopaedics, confirming that ISSs are still largely an unexplored frontier here. However, all of the studies targeted ions with a role also in musculoskeletal pathophysiology, thus relative ISSs could have a potential impact on orthopaedic diagnosis and treatment. Then, when described by the papers, ISSs' technological solutions were systematically evaluated. Finally, the main ISSs development targets for reaching orthopaedic clinical application were highlighted, including biocompatibility (e.g., implantability), long-term stability, calibration, and validation. Overcoming these challenges will enable ISSs to progress from laboratory prototypes to clinically viable tools, supporting the advancement of next-generation sensorised prostheses, fixation devices, and surgical instruments, and paving the way for predictive and personalised orthopaedic medicine.
JPMA. The Journal of the Pakistan Medical AssociationMuhammad Talal Ibrahim, Haider Sheraz, Muhammad Hamza Shuja, Hamza Imran, Anum Ali, Shahryar Noordin
Research articles are increasingly being disseminated on social media, which is not captured by traditional bibliometrics. The Article-Level Metric score captures the 'social' impact of such research articles as well. The current bibliometric analysis was planned to identify predictors of high Altmetric score in the top-cited articles related to orthopaedic surgery. Multilevel, mixedmethod, linear regression was employed to adjust for clustering of article-level and journal-level factors among 810 articles from 27 journals. The study was conducted at Aga Khan University, Karachi, Pakistan from June to December 2023. In multivariable, multilevel, linear regression, each additional citation led to a 0.33-unit increase in Article-Level Metric score (β=0.33, p=0.02), articles published in the Netherlands had a higher score by 10.6 units compared to Germany (β=10.60, p=0.01), and each additional unit increase in journal's impact factor increased the score by 1.73 units (β=1.73, p=0.04).
NEJM catalyst innovations in care deliveryPeter A Cole, Brian Cunningham, Mai Nguyen, Gennadiy Busel, L Pearce McCarty, Indraneel S Brahme, Sandy Vang, Jeffrey D Winter, Humza Shaikh
The NorthStar Trauma Network (NSTN) launched an initiative in 2018 to address systemic and local hospital challenges in the delivery of fracture care across a metropolitan area with a population of about 3 million. This regional fracture care model has expanded, serving seven hospitals in three health systems by 2023, including five participating community hospitals with level 2 or 3 trauma care centers and one level 1 pediatric hospital, all anchored by a level 1 university teaching trauma center. Recruitment and culture have been built around shared academic values, inclusive of investigation and education, promoting a surgeon retention rate of 100% for the first 6 years while sharing learned information and innovation that helps to drive impact in the local and larger community. Three aspects of the NSTN model, which is part of the HealthPartners system, differentiate it from other specialty or trauma care models; these are related to staffing, mission, and access. First, the NSTN model is designed to build trauma programs for hospitals rather than just providing call coverage; these programs are distinguished by their commitment of dedicated trauma-trained staff to network-member hospitals, which may be owned and operated by a variety of different - even competing - health systems. Second, the NSTN is anchored by one leadership team that promotes a collective academic mission among members; this is galvanized by the activities of the NorthStar Trauma Society, a related 501(c)(3) nonprofit organization focused on research, education, and innovation. Third, the NSTN model emphasizes subspecialist-level orthopedic trauma care (generally only seen at level 1 trauma centers) at level 2 and 3 trauma center hospitals, as well as level 1 pediatric hospitals, which expands access to care. This documentation of the evolution and methods of the NSTN may serve as a helpful example for other metropolitan communities with hospitals and health systems that seek to improve the quality of specialty-specific trauma care, the alignment of surgeons, and growth in surgical care.