Variation in surgical demand and time to hip fracture repair: a Canadian database study
Bibliographic record
Abstract
Abstract Background Competing demands for operative resources may affect time to hip fracture surgery. We sought to determine the time to hip fracture surgery by variation in demand in Canadian hospitals. Methods We obtained discharge abstracts of 151,952 patients aged 65 years or older who underwent surgery for a hip fracture between January, 2004 and December, 2012 in nine Canadian provinces. We compared median time to surgery (in days) when demand could be met within a two-day benchmark and when demand required more days, i.e. clearance time, to provide surgery, overall and stratified by presence of medical reasons for delay. Results For persons admitted when demand corresponded to a 2-day clearance time, 68% of patients underwent surgery within the 2-day benchmark. When demand corresponded to a clearance time of one week, 51% of patients underwent surgery within 2 days. Compared to demand that could be served within the two-day benchmark, adjusted median time to surgery was 5.1% (95% confidence interval [CI] 4.1–6.1), 12.2% (95% CI 10.3–14.2), and 22.0% (95% CI 17.7–26.2) longer, when demand required 4, 6, and 7 or more days to clear the backlog, respectively. After adjustment, delays in median time to surgery were similar for those with and without medical reasons for delay. Conclusion Increases in demand for operative resources were associated with dose-response increases in the time needed for half of hip fracture patients to undergo surgery. Such delays may be mitigated through better anticipation of day-to-day supply and demand and increased response capability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".