Intensivists' base specialty of training is associated with variations in mortality and practice patterns
Bibliographic record
Abstract
INTRODUCTION: Current evidence regarding whether the staffing of intensive care units (ICUs) with a trained Intensivist benefits patient outcomes is discordant. We sought to determine whether, among certified Intensivists, base specialty of training could contribute to variation in practice patterns and patient outcomes in ICUs. METHODS: The records of all patients who were admitted to one of three closed multi-system ICUs within tertiary care centers in the Calgary Health Region, Alberta, Canada, during a five year period were retrospectively reviewed. Outcomes for patients admitted by Intensivists with base training in General Internal Medicine, Pulmonary Medicine, or other eligible base specialties (Anesthesia, General Surgery, and Emergency Medicine combined) were compared. RESULTS: ICU mortality in the entire cohort (n = 9,808) was 17.2% and in-hospital mortality was 32.0%. After controlling for potential confounders, ICU mortality (odds ratio (OR): 0.69; 95% confidence interval (CI): 0.52 to 0.94) was significantly lower for patients admitted by Intensivists with Pulmonary Medicine as a base specialty of training, but not ICU length of stay (LOS) (coefficient: 0.11; -0.20 to 0.42) or hospital mortality (OR: 0.88; 0.68 to 1.13). There was no difference in ICU or hospital mortality or length of stay between the three base specialty groups for patients who were admitted and managed by a single Intensivist for their entire ICU admission (n = 4,612). However, we identified significant variation in practice patterns between the three specialty groups for the number of invasive procedures performed and decisions to limit life-sustaining therapies. CONCLUSIONS: Intensivists' base specialty of training is associated with practice pattern variations. This may contribute to differences in processes and outcomes of patient care.
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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 | 0.005 |
| 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 | 0.001 | 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".