ISQUA16-1366HIP FRACTURE MORTALITY BY TEACHING STATUS OF TREATING HOSPITAL
Bibliographic record
Abstract
There is inconsistent evidence for an association between treatment setting and hospital mortality after hip fracture. This study compares the risk of hospital death between patients treated in teaching and community hospitals, controlling for potential confounders and length of stay. Analysis of 167,816 hip fracture patients aged 65 years and older entered into Canadian acute hospital discharge abstracts from 2004-2012. Cumulative incidence of hospital death by in-patient day, accounting for discharge as a competing event for teaching and community hospitals. The cumulative incidence of hospital death at in-patient day 30 was lowest for teaching hospital admissions (7.3%) and highest for small community hospital admissions (11.5%). The adjusted odds of hospital death were 12% (95% CI 1.06–1.19), 25% (95% CI 1.17–1.34), and 64% (95% CI 1.50–1.79) higher for large, medium, and small community versus teaching hospital admissions. The adjusted odds of nonoperative death were 1.6 (95% CI 1.42–1.86), and 3.4 times (95% CI 2.96–3.94) higher for medium and small community versus teaching hospital admissions. The adjusted odds of postoperative death were 14% (95% CI 1.07–1.22) and 20% (95% CI 1.10–1.31) higher at large and medium community versus teaching hospitals. The adjusted odds of postoperative death were largest at small community hospitals (OR = 1.25, 95% CI 0.92–1.70).
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".