Structured Training to Improve Nontechnical Performance of Junior Surgical Residents in the Operating Room
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to evaluate the effectiveness of structured training on junior trainees' nontechnical performance in an operating room (OR) environment. BACKGROUND: Nontechnical skills (NTS) have been identified as critical competencies of surgeons in the OR, and regulatory bodies have mandated their integration in postgraduate surgical curricula. Strong evidence supporting the effectiveness of curricular NTS training, however, is lacking. METHODS: Junior surgical residents were randomized to receive either conventional residency training or additional NTS training in a 2-month curriculum. Learning was assessed through a knowledge quiz and an attitudes survey. Nontechnical performance was evaluated by blinded assessment of standardized OR crisis simulations at baseline (BL) and posttraining (PT) using the Nontechnical Skills for Surgeons (NOTSS) and Objective Structured Assessment of Nontechnical Skills (OSANTS) rating systems. Results are reported as median (interquartile ranges). RESULTS: Of 23 participants, 22 completed BL and PT assessments. Groups were equal at BL. At PT, curriculum-trained residents (n = 11) scored higher than conventionally trained residents (n = 11) in knowledge [12 (11-13) vs 8 (6-10), P < 0.001] and attitudes [4.58 (4.37-4.73) vs 4.20 (4.00-4.50), P = 0.008] about NTS. In a simulated OR, nontechnical performance of curriculum-trained residents improved significantly from BL to PT [NOTSS: 10 (7-11) vs 13 (10-15), P = 0.012; OSANTS: 23 (17-28) vs 31 (25-33), P = 0.012] whereas conventionally trained residents did not improve [NOTSS: 10 (10-13) vs 11 (9-14), P = 1.00; OSANTS: 26 (24-32) vs 24 (23-32), P = 0.713]. CONCLUSIONS: The results demonstrate the effectiveness of structured curricular training in improving nontechnical performance in the first year of surgical residency, supporting routine implementation of nontechnical components in postgraduate surgical curricula.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".