Reporting and evaluating wait times for urgent hip fracture surgery in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Although a delay of 24 hours for hip fracture repair is associated with medical complications and costs, it is unknown how long patients wait for surgery for hip fracture. We describe novel methods for measuring exact urgent and emergent surgical wait times (in hours) and the factors that influence them. METHODS: Adults aged 45 years and older who underwent surgery for hip fracture (the most common urgently performed procedure) in Ontario, Canada, between 2009 and 2014 were eligible. Validated data from linked health administrative databases were used. The primary outcome was the time elapsed from hospital arrival recorded in the National Ambulatory Care Reporting System until the time of surgery recorded in the Discharge Abstract Database (in hours). The influence of patient, physician and hospital factors on wait times was investigated using 3-level, hierarchical linear regression models. RESULTS: Among 42 230 patients with hip fracture, the mean (SD) wait time for surgery was 38.76 (28.84) hours, and 14 174 (33.5%) patients underwent surgery within 24 hours. Variables strongly associated with delay included time for hospital transfer (adjusted increase of 26.23 h, 95% CI 25.38 to 27.01) and time for preoperative echocardiography (adjusted increase of 18.56 h, 95% CI 17.73 to 19.38). More than half of the hospitals (37 of 72, 51.4%), compared with 4.8% of surgeons and 0.2% of anesthesiologists, showed significant differences in the risk-adjusted likelihood of delayed surgery. INTERPRETATION: Exact wait times for urgent and emergent surgery can be measured using Canada's administrative data. Only one-third of patients received surgery within the safe time frame (24 h). Wait times varied according to hospital and physician factors; however, hospital factors had a larger impact.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".