Teaching cognitive skills improves learning in surgical skills courses: a blinded, prospective, randomized study.
Bibliographic record
Abstract
OBJECTIVE: To investigate the teaching of cognitive skills within a technical skills course, we carried out a blinded, randomized prospective study. METHODS: Twenty-one junior residents (postgraduate years 1-3) from a single program at a surgical-skills training centre were randomized to 2 surgical skills courses teaching total knee arthroplasty. One course taught only technical skill and had more repetitions of the task (5 or 6). The other focused more on developing cognitive skills and had fewer task repetitions (3 or 4). All were tested with the Objective Structured Assessment of Technical Skill (OSATS) both before and after the course, as well as a pre- and postcourse error-detection exam and a postcourse exam with multiple-choice questions (MCQs) to test their cognitive skills. RESULTS: Both groups' technical skills as assessed by OSATS were equivalent, both pre- and postcourse. Taking their courses improved the technical skills of both groups (OSATS, p < 0.01) over their pre-course scores. Both groups demonstrated equivalent levels of knowledge on the MCQ exam, but the cognitive group scored better on the error-detection test (p = 0.02). CONCLUSIONS: Cognitive skills training enhances the ability to correctly execute a surgical skill. Furthermore, specific training and practice are required to develop procedural knowledge into appropriate cognitive skills. Surgeons need to be trained to judge the correctness of their actions.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".