Validity Evidence for Procedure-specific Competence Assessment Tools in Orthopaedic Surgery: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Competency-based training requires frequent assessment of residents' skills to determine clinical competence. This study reviews existing literature on procedure-specific competence assessment tools in orthopaedic surgery. METHODS: A systematic search of eight databases up to May 2023 was conducted. Two reviewers independently assessed validity evidence and educational utility of each assessment tool and evaluated studies' methodological quality. RESULTS: Database searching identified 2,556 unique studies for title and abstract screening. Full texts of 290 studies were reviewed; 17 studies met the inclusion criteria. Bibliography review identified another five studies, totaling 22 studies examining 24 assessment tools included in the analysis. These tools assessed various orthopaedic surgery procedures within trauma, sports medicine, spine, and upper extremity. Overall validity evidence was low across all studies, and was lowest for consequences and highest for content. Methodological quality of studies was moderate. Educational utility assessment was not explicitly done for most tools. DISCUSSION: The paucity of current procedure-specific assessment tools in orthopaedic surgery lacks the validity evidence required to be used reliably in high-stake summative assessments. Study strengths include robust methodology and use of an evidence-based validity evidence framework. Poor-quality existing evidence is a limitation and highlights the need for evidence-based tools across more subspecialties.
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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 | 0.053 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".