Objective Structured Clinical Examination (OSCE)-based Assessment of the Advanced Trauma Life Support (ATLS) Course in Iran.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of advance trauma life support (ATLS®) training on general surgery residents clinical reasoning skills using the national boards-style objective structured clinical examination (OSCE). METHODS: This cross-sectional single-center study was conducted in Shiraz University of Medical Sciences including 51 surgery residents that participated in a mandatory national board style OSCE between May 2014 and May 2015. OSCE scores of two groups of general surgery residents including 23 ATLS® trained and 28 non-ATLS® trained were compared using Mann-Whitney U test. The exam was graded out of 20 points and the passing score was ≥14 including 40% trauma cases. RESULTS: There were 8(15.7%) women and 43(84.3%) men among the participants with mean age of 31.12 ± 2.69 and 33.67 ± 4.39 years in women and men respectively. Overall 7 (87.5%) women and 34 (79.07%) men passed the OSCE. The trauma section OSCE score was significantly higher in the ATLS® trained participants when compared to non-ATLS®(7.79 ± 0.81vs.6.90 ± 1.00; p=0.001). In addition, the total score was also significantly higher in ATLS® trained residents (16.07 ± 1.41 vs. 14.60 ± 1.40; p=0.001). There was no association between gender and ATLS® score (p=0.245) or passing the OSCE (p=0.503). CONCLUSION: ATLS® training is associated with improved overall OSCE scores of general surgery residents completing the board examinations suggesting a positive transfer of ATLS learned skills to management of simulated surgical patients including trauma cases.
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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 | 0.001 | 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) | 0.001 | 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".