Variation in surgical demand and time to hip fracture repair: a Canadian database study
Bibliographic record
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 | 0.002 | 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) | 0.002 | 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".