Reduced time to surgery improves mortality and length of stay following hip fracture: results from an intervention study in a Canadian health authority
Bibliographic record
Abstract
BACKGROUND: Existing literature demonstrating the negative impact of delayed hip fracture surgery on mortality consists largely of observational studies prone to selection bias and may overestimate the negative effects of delay. We conducted an intervention study to assess initiatives aimed at meeting a 48-hour benchmark for hip fracture surgery to determine if the intervention achieved a reduction in time to surgery, and if a general reduction in time to surgery improved mortality and length of stay. METHODS: We compared time to surgery, length of stay and mortality between pre- and postintervention patients with a hip fracture using the Kaplan-Meier estimator and Cox proportional hazards model adjusting for age, sex, comorbidities, type of surgery and year. RESULTS: We included 3525 pre- and 3007 postintervention patients aged 50 years or older. The proportion of patients receiving surgery within the benchmark increased from 66.8% to 84.6%, median length of stay decreased from 13.5 to 9.7 days, and crude in-hospital mortality decreased from 9.6% to 6.8% (all p < 0.001). Adjusted analyses revealed reduced mortality in hospital (hazard ratio [HR] 0.68, 95% confidence interval [CI] 0.57-0.81) and at 1 year (HR 0.87, 95%CI 0.79-0.96). Independent of the intervention period, having surgery within 48 hours demonstrated decreased adjusted risk of death in hospital (HR 0.51, 95%CI 0.41-0.63) and at 1 year postsurgery (HR 0.72, 95% CI 0.64-0.80). CONCLUSION: Coordinated, region-wide efforts to improve timeliness of hip fracture surgery can successfully reduce time to surgery and appears to reduce length of stay and adjusted mortality in hospital and at 1 year.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".