Hospital mortality after hip fracture surgery in relation to length of stay by care delivery factors
Bibliographic record
Abstract
Two hypotheses were offered for the effect of shorter hospital stays on mortality after hip fracture surgery: worsening the quality of care and shifting death occurrence to postacute settings.We tested whether the risk of hospital death after hip fracture surgery differed across years when postoperative stays shortened, and whether care factors moderated the association.Analysis of acute hospital discharge abstracts for subgroups defined by hospital type, bed capacity, surgical volume, and admission time.153,917 patients 65 years or older surgically treated for first hip fracture.Risk of hospital death.We found a decrease in the 30-day risk of hospital death from 7.0% (95%CI: 6.6-7.5) in 2004 to 5.4% (95%CI: 5.0-5.7) in 2012, with an adjusted odds ratio [OR] 0.71 (95%CI: 0.63-0.80). In subgroup analysis, only large community hospitals showed the reduction of ORs by calendar year. No trend was observed in teaching and medium community hospitals. By 2012, the risk of death in large higher volume community hospitals was 34% lower for weekend admissions, OR = 0.66 (95%CI: 0.46-0.95) and 39% lower for weekday admissions, OR = 0.61 (95%CI: 0.40-0.91), compared to 2004. In large lower volume community hospitals, the 2012 risk was 56% lower for weekend admissions, OR = 0.44 (95%CI: 0.26-0.75), compared to 2004.The risk of hospital death after hip fracture surgery decreased only in large community hospitals, despite universal shortening of hospital stays. This supports the concern of worsening the quality of hip fracture care due to shorter stays.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 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".