Hip fracture outcomes, risk prediction, and hospital comparisons: a population-based study in Ontario Canada
Bibliographic record
Abstract
INTRODUCTION: Hip fracture repair is one of the most common urgent procedures performed in hospitals. Having a high burden of mortality, hip fracture repair is frequently targeted for health system quality improvement and hospital performance monitoring. In the present study, we measure hospital variability and explore factors associated with 90-day mortality and the time from emergency department (ED) visit until surgery. METHODS: Patients were 50-105 years of age at the time of their hip fracture surgery between fiscal years 2015/16 and 2023/24 in Ontario Canada. Hospital variation was measured using random intercept models, risk-adjusted mortality rates, and funnel plots. Risk-adjusted mortality was computed as observed/expected (O/E) ratios multiplied by the population mortality rate. Expected mortality was estimated using logistic regression or CatBoost machine learning methods adjusted for age, sex, comorbidity, and other measures of healthcare utilization. Funnel plots were presented using crude and risk-adjusted mortality by hospital volume. Bootstrap sampling was used to compute 95 % confidence intervals. RESULTS: A total 12,607 deaths (12.1 %) occurred within 90 days of hip fracture repair (N = 103,887), 4488 (36 %) of which occurred in hospital. Hospitals only accounted for 0.6 % of the total variation in 90-day mortality. Other predictors of mortality included older age, male, higher comorbidity score, facility transfer, pre-operative anemia, home care, residence in long-term care, no prior receipt of anti-osteoarthritic medication, and no previous bone-mineral density scan (p < 0.0001 for all). Hospitals accounted for 9.2 % of the variability in the odds of receiving surgery within 48 h of ED visit. There was no clear cut-point of the time from ED arrival until surgery on the risk of 90-day mortality. There was no ecological association between hospital performance on timeliness (receipt of surgery within 48 h) and performance on 90-day mortality. CONCLUSION: There was little hospital variation in 90-day mortality. Using three different approaches, there were a few hospitals that consistently stood out as performing better/worse than expected. There was more substantial variation in the time until treatment across hospitals, but the relationship between the time until surgery and 90-day mortality was tenuous.
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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 | 0.000 |
| 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".