Do Factors Other Than Trauma Volume Affect Attrition of ATLS-Acquired Skills?
Bibliographic record
Abstract
BACKGROUND: We previously demonstrated that trauma patient volume affects attrition rate of Advanced Trauma Life Support (ATLS)-acquired skills. This study assesses the possible roles of age, gender, and practice specialty on attrition of these skills over 8 years. METHODS: Cognitive (assessed by the 40-item Multiple Choice Question Examination [MCQE]) and clinical (assessed by four trauma Objective Structured Clinical Examination [OSCE] stations) skills performance were compared among physicians who completed the ATLS course 0 months, 6 months, 2 years, 4 years, 6 years, and 8 years previously. The physicians were further divided into the following groups: age < 32 years (n = 72) or 32 years or older (n = 72), gender (41 women and 103 men), and specialty (54 surgeons, 90 nonsurgeons, and 22 general surgeons). Multivariate analysis of variance was used for statistical comparison over time and unpaired t tests for between-group comparisons for each time period, with p < 0.05 being considered statistically significant. RESULTS: Regarding age, MCQE decreased from 82.3 +/- 2.8% to 62.7 +/- 3.0% (mean +/- SD) for age < 32 and from 84.1 +/- 3.6% to 62.8 +/- 2.1% for age 32 or older (p = not significant). Overall OSCE score (maximum, 20) decreased similarly for age < 32 (18.0 +/- 0.4 to 13.6 +/- 2.0) and age > 32 or older (18.0 +/- 0.3 to 12.4 +/- 1.3). Decrease in Priorities and Organized Approach scores also showed no differences between the groups. Regarding gender, MCQE decreased similarly in both groups (women, 81.5 +/- 2.2% to 64.4 +/- 2.4%; men, 83.3 +/- 3.2% to 64.1 +/- 4.2%) and so did OSCE, Priorities, and Organized Approach scores. Regarding specialty, surgeons (83.0 +/- 3.1% to 66.1 +/- 4.5%), nonsurgeons (82.9 +/- 3.2% to 63.3 +/- 3.9%), and general surgeons (82.5 +/- 3.5% to 63.8 +/- 5.3%) showed similar decreases in MCQE scores. Overall OSCE scores and Priority and Approach scores decreased similarly in all specialty groups. When trauma volume was controlled, there was still no difference in attrition rate between surgeons and nonsurgeons. CONCLUSION: Trauma patient volume is the most critical determinant of attrition rate of ATLS-acquired skills. Gender, age (at time of taking the course), and practice specialty do not alter this attrition rate.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".