Reflections on current methods for evaluating skills during joint replacement surgery
Bibliographic record
Abstract
Valid and reliable techniques for assessing performance are essential to surgical education, especially with the emergence of competency-based frameworks. Despite this, there is a paucity of adequate tools for the evaluation of skills required during joint replacement surgery. In this scoping review, we examine current methods for assessing surgeons' competency in joint replacement procedures in both simulated and clinical environments. The ability of many of the tools currently in use to make valid, reliable and comprehensive assessments of performance is unclear. Furthermore, many simulation-based assessments have been criticised for a lack of transferability to the clinical setting. It is imperative that more effective methods of assessment are developed and implemented in order to improve our ability to evaluate the performance of skills relating to total joint replacement. This will enable educators to provide formative feedback to learners throughout the training process to ensure that they have attained core competencies upon completion of their training. This should help ensure positive patient outcomes as the surgical trainees enter independent practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.382 | 0.488 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.015 | 0.009 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".