Bibliographic record
Abstract
ABSTRACT—Restricted government spending along with universal health insurance has led to longer queues for surgical procedures in Canada versus the United States. Yet it is unclear whether these treatment delays affect health outcomes. This paper tests this hypothesis by comparing the determinants of wait time for hip-fracture surgery and its impact on postsurgery length of stay and inpatient mortality in Canada and the United States. Hazards for surgery/no surgery and discharge alive versus dead are modeled using a competing-risks model. Day of the week of admission is used to help identify the surgery wait-time distribution. We control for unobserved (to the econometrician) health status which may affect wait times and outcomes by assuming a semiparametric distribution for unobserved heterogeneity. We nd that predicted hazards for inpatient mortality are virtually identical in Canada and the United States. Yet wait times for surgery are longer in Canada, and surgery delay has a signi cant impact on postsurgery length of stay in both countries. However, the magnitude of this effect is small relative to other patient and hospital-speci c factors. Focusing attention on treatment delays as a weakness in the Canadian health care system may be misleading policymakers from hospital-speci c inefficiencies that may have more-important implications for health care costs and patient welfare. I.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.855 | 0.809 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".