The Effect of a Multidisciplinary Trauma Team Leader Paradigm at a Tertiary Trauma Center: 10-Year Experience
Bibliographic record
Abstract
Background. To illustrate the impact of the implementation of a multidisciplinary TTL program in 2005 on the mortality of trauma patients in a level 1 trauma center as well as admission rates and length of stay. Methods. Retrospective observational study of all trauma patients included in the provincial trauma database at the Montreal General Hospital between 1998 and 2015. The primary outcome studied was in-hospital mortality. The secondary outcomes studied were hospital and intensive care unit (ICU) rates of admission and hospital and ICU length of stay. Results. 24,107 patients were included. We observed a statistically significant reduction in mortality of 1.25% or a relative reduction of 16% ( p value = 0.0058; rate ratio 0.844 (95% CI 0.747–0.952)). ICU admissions were also significantly reduced where we observed a statistically significant absolute reduction of 4.46% or a relative reduction of 14% ( p value = 8.38 × 10−7; rate ratio 0.859 (95% CI 0.808–0.912)). The ICU length of stay was increased by 0.91 days or 19.03% ( p value = 0.016 (95% CI 0.167–1.655)). There was no observed change in overall length of stay (13.97 days pre-TTL and 12.91 post-TTL ( p value = 0.13; estimate −1.053 (95% CI −2.424–0.318))). Conclusions. This article suggests that multidisciplinary TTL model may be beneficial in the care of trauma patients. Further subgroup analysis may help determine which patients could benefit more.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".