Hospital and Intensive Care Unit Length of Stay for Injury Admissions
Bibliographic record
Abstract
OBJECTIVE: To assess the variation in hospital and intensive care unit (ICU) length of stay (LOS) for injury admissions across Canadian provinces and to evaluate the relative contribution of patient case mix and treatment-related factors (intensity of care, complications, and discharge delays) to explaining observed variations. BACKGROUND: Identifying unjustified interprovider variations in resource use and the determinants of such variations is an important step towards optimizing health care. METHODS: We conducted a multicenter, retrospective cohort study on admissions for major trauma (injury severity score >12) to level I and II trauma centers across Canada (2006-2012). We used data from the Canadian National Trauma Registry linked to hospital discharge data to compare risk-adjusted hospital and ICU LOS across provinces. RESULTS: Risk-adjusted hospital LOS was shortest in Ontario (10.0 days) and longest in Newfoundland and Labrador (16.1 days; P < 0.001). Risk-adjusted ICU LOS was shortest in Québec (4.4 days) and longest in Alberta (6.1 days; P < 0.001). Patient case-mix explained 32% and 8% of interhospital variations in hospital and ICU LOS, respectively, whereas treatment-related factors explained 63% and 22%. CONCLUSIONS: We observed significant variation in risk-adjusted hospital and ICU LOS across trauma systems in Canada. Provider ranks on hospital LOS were not related to those observed for ICU LOS. Treatment-related factors explained more interhospital variation in LOS than patient case-mix. Results suggest that interventions targeting reductions in low-value procedures, prevention of adverse events, and better discharge planning may be most effective for optimizing LOS for injury admissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".