Knowing the operative game plan: a novel tool for the assessment of surgical procedural knowledge
Bibliographic record
Abstract
BACKGROUND: What is the source of inadequate performance in the operating room? Is it a lack of technical skills, poor judgment or a lack of procedural knowledge? We created a surgical procedural knowledge (SPK) assessment tool and evaluated its use. METHODS: We interviewed medical students, residents and training program staff on SPK assessment tools developed for 3 different common general surgery procedures: inguinal hernia repair with mesh in men, laparoscopic cholecystectomy and right hemicolectomy. The tools were developed as a step-wise assessment of specific surgical procedures based on techniques described in a current surgical text. We compared novice (medical student to postgraduate year [PGY]-2) and expert group (PGY-3 to program staff) scores using the Mann-Whitney U test. We calculated the total SPK score and defined a cut-off score using receiver operating characteristic analysis. RESULTS: In all, 5 participants in 7 different training groups (n = 35) underwent an interview. Median scores for each procedure and overall SPK scores increased with experience. The median SPK for novices was 54.9 (95% confidence interval [CI] 21.6-58.8) compared with 98.05 (95% CP 94.1-100.0) for experts (p = 0.012). The SPK cut-off score of 93.1 discriminates between novice and expert surgeons. CONCLUSION: Surgical procedural knowledge can reliably be assessed using our SPK assessment tool. It can discriminate between novice and expert surgeons for common general surgical procedures. Future studies are planned to evaluate its use for more complex procedures.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".