Resource use for older people hospitalised due to injury in a Canadian integrated trauma system: a retrospective multicenter cohort study
Bibliographic record
Abstract
BACKGROUND: Injuries represent one of the leading causes of preventable morbidity and mortality. For countries with ageing populations, admissions of injured older patients are increasing exponentially. Yet, we know little about hospital resource use for injured older patients. Our primary objective was to evaluate inter-hospital variation in the risk-adjusted resource use for injured older patients. Secondary objectives were to identify the determinants of resource use and evaluate its association with clinical outcomes. METHODS: We conducted a multicenter retrospective cohort study of injured older patients (≥65 years) admitted to any trauma centres in the province of Quebec (2013-2016, N = 33,184). Resource use was estimated using activity-based costing and modelled with multilevel linear models. We conducted separate subgroup analyses for patients with trauma and fragility fractures. RESULTS: Risk-adjusted resource use varied significantly across trauma centres, more for older patients with fragility fractures (intra-class correlation coefficients [ICC] = 0.093, 95% CI [0.079, 0.102]) than with trauma (ICC = 0.047, 95% CI = 0.035-0.051). Risk-adjusted resource use increased with age, and the number of comorbidities, and varied with discharge destination (P < 0.001). Higher hospital resource use was associated with higher incidence of complications for trauma (Pearson correlation coefficient [r] = 0.5, 95% CI = 0.3-0.7) and fragility fractures (r = 0.5, 95% CI = 0.3-0.7) and with higher mortality for fragility fractures (r = 0.4, 95% CI = 0.2-0.6). CONCLUSIONS: We observed significant inter-hospital variations in resource use for injured older patients. Hospitals with higher resource use did not have better clinical outcomes. Hospital resource use may not always positively impact patient care and outcomes. Future studies should evaluate mechanisms, by which hospital resource use impacts 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".