Mortality effects of timing alternatives for hip fracture surgery
Bibliographic record
Abstract
BACKGROUND: The appropriate timing of hip fracture surgery remains a matter of debate. We sought to estimate the effect of changes in timing policy and the proportion of deaths attributable to surgical delay. METHODS: We obtained discharge abstracts from the Canadian Institute for Health Information for hip fracture surgery in Canada (excluding Quebec) between 2004 and 2012. We estimated the expected population-average risks of inpatient death within 30 days if patients were surgically treated on day of admission, inpatient day 2, day 3 or after day 3. We weighted observations with the inverse propensity score of surgical timing according to confounders selected from a causal diagram. RESULTS: Of 139 119 medically stable patients with hip fracture who were aged 65 years or older, 32 120 (23.1%) underwent surgery on admission day, 60 505 (43.5%) on inpatient day 2, 29 236 (21.0%) on day 3 and 17 258 (12.4%) after day 3. Cumulative 30-day in-hospital mortality was 4.9% among patients who were surgically treated on admission day, increasing to 6.9% for surgery done after day 3. We projected an additional 10.9 (95% confidence interval [CI] 6.8 to 15.1) deaths per 1000 surgeries if all surgeries were done after inpatient day 3 instead of admission day. The attributable proportion of deaths for delays beyond inpatient day 2 was 16.5% (95% CI 12.0% to 21.0%). INTERPRETATION: Surgery on admission day or the following day was estimated to reduce postoperative mortality among medically stable patients with hip fracture. Hospitals should expedite operating room access for patients whose surgery has already been delayed for nonmedical reasons.
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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 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".