Interhospital Variations in Resource Use Intensity for In-hospital Injury Deaths: A Retrospective Multicenter Cohort Study.
Bibliographic record
Abstract
OBJECTIVE: Evaluate interhospital variation in resource use for in-hospital injury deaths. BACKGROUND: Significant variation in resource use for end-of-life care has been observed in the US for chronic diseases. However, there is an important knowledge gap on end-of-life resource use for trauma patients. METHODS: We conducted a multicenter, retrospective cohort study of injury deaths following hospitalization in any of the 57 trauma centers in a Canadian trauma system (2013-2016). Resource use intensity was measured using activity-based costing (2016 $CAN) according to time of death (72 h, 3-14 d, ≥14 d). We used multilevel log-linear regression to model resource use and estimated interhospital variation using intraclass correlation coefficients (ICC). RESULTS: Our study population comprised 2044 injury deaths. Variation in resource use between hospitals was observed for all 3 time frames (ICC = 6.5%, 6.6%, and 5.9% for < 72 h, 3-14 d, and ≥14 d, respectively). Interhospital variation was stronger for allied health services (ICC = 18 to 26%), medical imaging (ICC = 4 to 10%), and the ICU (ICC = 5 to 6%) than other activity centers. We observed stronger interhospital variation for patients < 65 years of age (ICC = 11 to 34%) than those ≥65 (ICC = 5 to 6%) and for traumatic brain injury (ICC = 5 to 13%) than other injuries (ICC = 1 to 8%). CONCLUSIONS: We observed variation in resource use intensity for injury deaths across trauma centers. Strongest variation was observed for younger patients and those with traumatic brain injury. Results may reflect variation in level of care decisions and the incidence of withdrawal of life-sustaining therapies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".