Assessment of Technical Skills Competence in the Operating Room: A Systematic and Scoping Review
Bibliographic record
Abstract
PURPOSE: While academic accreditation bodies continue to promote competency-based medical education (CBME), the feasibility of conducting regular CBME assessments remains challenging. The purpose of this study was to identify evidence pertaining to the practical application of assessments that aim to measure technical competence for surgical trainees in a nonsimulated, operative setting. METHOD: In August 2016, the authors systematically searched Medline, Embase, and the Cochrane Database of Systematic Reviews for English-language, peer-reviewed articles published in or after 1996. The title, abstract, and full text of identified articles were screened. Data regarding study characteristics, psychometric and measurement properties, implementation of assessment, competency definitions, and faculty training were extracted. The findings from the systematic review were supplemented by a scoping review to identify key strategies related to faculty uptake and implementation of CBME assessments. RESULTS: A total of 32 studies were included. The majority of studies reported reasonable scores of interrater reliability and internal consistency. Seven articles identified minimum scores required to establish competence. Twenty-five articles mentioned faculty training. Many of the faculty training interventions focused on timely completion of assessments or scale calibration. CONCLUSIONS: There are a number of diverse tools used to assess competence for intraoperative technical skills and a lack of consensus regarding the definition of technical competence within and across surgical specialties. Further work is required to identify when and how often trainees should be assessed and to identify strategies to train faculty to ensure timely and accurate assessment.
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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 | 0.024 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".