Teaching Technical Skills
Bibliographic record
Abstract
The purpose of this study was to determine whether surgical residents could significantly improve their performance on a specific surgical procedure after a brief practice session with feedback. Attending plastic surgeons, using valid and reliable checklists and global rating scales, objectively assessed 37 junior surgical residents while performing two-flap Z-plasties on pig thighs (one before and one after a one-on-one, 5-minute practice session with feedback). The total cost per resident was $1.00 (Canadian currency). After the practice session, total checklist scores improved from 7.3 (range, 1 to 9) to 7.9 (range, 5 to 9), and the total global rating scores improved from 29.1 (range, 13 to 41) to 31.9 (range, 19 to 43). Paired Student's t tests revealed significant improvement in both the mean total checklist scores (p < 0.05) and mean total global rating scores (p < 0.01). Also, the global rating score for appearance and quality of the final surgical product significantly improved from 2.7 to 3.3 after the practice session (p < 0.01). There were no significant differences in performance scores between men and women, between first-year and second-year residents, with residents' previous experience with the Z-plasty procedure, or with resident's base surgical specialties. The results of this prospective study indicate that training on a simple and portable model with very brief individualized practice and feedback is an effective and inexpensive way of improving resident performance. A 5-minute practice session with a surgical trainee before performing a procedure on a living patient may significantly improve the patient's surgical performance and produce a superior result.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.028 |
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".