Examining systemic differences in mortality after hip repair: a comparative analysis of 30- and 180-day adjusted mortality rates in five health systems
Bibliographic record
Abstract
Outcomes after a hip repair in the older adult population are highly dependent on patients' characteristics. However, contextual factors such as the hospital of treatment may have an impact not sufficiently studied. We aimed to elicit the effect of hospital providers on all-cause-adjusted mortality rates after hip fracture repair. Observational study on virtually all potentially eligible hip fracture patients treated in 2240 hospitals from Ontario (Canada), Aragon (Spain), Finland, Sweden, and the USA (40 states). The primary endpoint was the risk-adjusted all-cause mortality after hip repair measured 30 days and 180 days after surgery. Following a federated approach, GAMM-logit models were run for each region. Median odds ratio (MOR) were estimated to elicit the variation at hospital level. The study included 535 519 hip repairs. The overall predicted 30-day adjusted mortality rate was 40.5 per 1000 hip repair episodes; 136.3 per 1000 hip repair episodes in the 180-day adjusted mortality rate. 30- and 180-day adjusted mortality rates were larger within the regions than across regions. Variance in patients' mortality at the hospital provider accounted for MOR: 1.43 in 30-day mortality and MOR: 1.35 in 180-day mortality. Beyond differences in the individual risk of death, our study found wide systemic variations in mortality rates in older adult patients exposed to hip fracture repair attributable to the hospital of treatment. Our results call for a reorientation of care pathways after hip repair in frail patients, both in the short- and the long-term.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".