Medicine versus surgery/anesthesiology intensivists: a retrospective review and comparison of outcomes in a mixed medical–surgical–trauma ICU
Bibliographic record
Abstract
BACKGROUND: With various types of complex patients being treated in a mixed medical- surgical- trauma intensive care unit (ICU), we hypothesized that there should be no difference in patient mortality with respect to the core training of the intensivist. METHODS: We reviewed the cases of all patients admitted to a mixed medical-surgical-trauma ICU at a Canadian university teaching hospital in 2007. Patients were assigned to 1 of 2 treatment groups (internal medicine, surgery/anesthesiology) based on the treating intensivist's training. Our primary outcome was to compare patient mortality in the ICU between the groups. We used generalized estimating equations to determine 10-day mortality after admission to the ICU. A multivariate Cox hazard model was used to determine statistical significance and 95% confidence intervals (CIs) for 11- to 60-day mortality in the ICU. RESULTS: A total of 961 patients were admitted from January to December, 2007. We found no significant difference between the groups in 10-day mortality (odds ratio 0.73, 95% CI 0.46-1.18, p = 0.20) and 11- to 60-day mortality (hazard ratio 1.43, 95% CI 0.62-3.30, p = 0.40) after admission to the ICU. CONCLUSION: In a large university trauma centre that operates a mixed medicine- surgical-trauma ICU, there was no significant difference in mortality between patients managed by intensivists with core training in internal medicine and those managed by intensivists with training in surgery/anesthesiology.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".