An Objective Assessment of the Surgical Trainee in an Urban Trauma Unit in South Africa: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Surgical outcomes are provider specific. This prospective audit describes the surgical activity of five general surgery residents on their trauma surgery rotation. It was hypothesized that the operating surgical trainee is an independent risk factor for adverse outcomes following major trauma. MATERIALS AND METHODS: This is a prospective cohort study. All patients admitted, over a 6-month period (August 2014-January 2015), following trauma requiring a major operation performed by a surgical trainee at Groote Schuur Hospital's trauma unit in South Africa were included. Multiple logistic regression models were built to compare risk-adjusted surgical outcomes between trainees. The primary outcome measure was major in-hospital complications. RESULTS: A total of 320 major operations involving 341 procedures were included. The mean age was 28.49 years (range 13-64), 97.2 % were male with a median ISS of 9 (IQR 1-41). Mechanism of injury was penetrating in 93.42 % of cases of which 51.86 % were gunshot injuries. Surgeon A consistently had the lowest risk-adjusted outcomes and was used as the reference for all outcomes in the regression models. Surgeon B, D, and E had statistically significant higher rates of major in-hospital complications than Surgeon A and C, after adjusting for multiple confounders. The final model used to calculate the risk estimates for the primary outcome had a ROC of 0.8649. CONCLUSION: Risk-adjusted surgical outcomes vary by operating surgical trainee. The analysis thereof can add value to the objective assessment of a surgical trainee.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".