In-hospital mortality after hip fracture by treatment setting
Bibliographic record
Abstract
BACKGROUND: Where patients with hip fracture undergo treatment may influence their outcome. We compared the risk of in-hospital death after hip fracture by treatment setting in Canada. METHODS: We examined all discharge abstracts from the Canadian Institute for Health Information with diagnosis codes for hip fracture involving patients 65 years and older who were admitted to hospital with a nonpathological first hip fracture between Jan. 1, 2004, and Dec. 31, 2012, in Canada (excluding Quebec). We compared the risk of in-hospital death, overall and after surgery, between teaching hospitals and community hospitals of various bed capacities, accounting for variation in length of stay. RESULTS: Compared with the number of deaths per 1000 admissions at teaching hospitals, there were an additional 3 (95% confidence interval [CI] 1-6), 14 (95% CI 10-18) and 43 (95% CI 35-51) deaths per 1000 admissions at large, medium and small community hospitals, respectively. For the risk of in-hospital death overall, the adjusted odds ratios (ORs) were 1.05 (95% CI 0.99-1.11), 1.16 (95% CI 1.09-1.24) and 1.44 (95% CI 1.31-1.57) at large, medium and small community hospitals, respectively, compared with teaching hospitals. For the risk of postsurgical death in hospital, the adjusted ORs were 1.06 (95% CI 1.00-1.13), 1.13 (95% CI 1.04-1.23) and 1.18 (95% CI 0.87-1.60) at large, medium and small community hospitals, respectively. INTERPRETATION: Compared with teaching hospitals, the risk of in-hospital death among patients with hip fracture was higher at medium and small community hospitals, and the risk of in-hospital death after surgery was higher at medium community hospitals. No differences were found between teaching and large community hospitals. Future research should examine the role of volume, demand and bed occupancy for observed differences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".