Assessing Technical Competence in Surgical Trainees
Bibliographic record
Abstract
OBJECTIVE: To systematically examine the literature describing the methods by which technical competence is assessed in surgical trainees. BACKGROUND: The last decade has witnessed an evolution away from time-based surgical education. In response, governing bodies worldwide have implemented competency-based education paradigms. The definition of competence, however, remains elusive, and the impact of these education initiatives in terms of assessment methods remains unclear. METHODS: A systematic review examining the methods by which technical competence is assessed was conducted by searching MEDLINE, EMBASE, PsychINFO, and the Cochrane database of systematic reviews. Abstracts of retrieved studies were reviewed and those meeting inclusion criteria were selected for full review. Data were retrieved in a systematic manner, the validity and reliability of the assessment methods was evaluated, and quality was assessed using the Grading of Recommendations Assessment, Development and Evaluation classification. RESULTS: Of the 6814 studies identified, 85 studies involving 2369 surgical residents were included in this review. The methods used to assess technical competence were categorized into 5 groups; Likert scales (37), benchmarks (31), binary outcomes (11), novel tools (4), and surrogate outcomes (2). Their validity and reliability were mostly previously established. The overall Grading of Recommendations Assessment, Development and Evaluation for randomized controlled trials was high and low for the observational studies. CONCLUSIONS: The definition of technical competence continues to be debated within the medical literature. The methods used to evaluate technical competence predominantly include instruments that were originally created to assess technical skill. Very few studies identify standard setting approaches that differentiate competent versus noncompetent performers; subsequently, this has been identified as an area with great research potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".