Regionalization of Hip Fracture Care in Five High‐Income Countries
Bibliographic record
Abstract
OBJECTIVE: To describe differences in regionalization of hip fracture care and the volume-outcome relationship in five countries. STUDY SETTING AND DESIGN: We conducted a population-based cross-sectional cohort study in Canada, Israel, the Netherlands, Taiwan, and the United States. Within each country, we stratified patients into quintiles based upon the volume of hip fractures in the hospital where they were treated. We measured regionalization by the proportion of acute-care hospitals that treated patients with hip fractures and summarized the hospital volume distribution by the ratio of hip fracture volumes for high-volume hospitals versus low-volume hospitals. We then examined age- and sex-standardized outcomes and treatment for patients treated at high-volume and low-volume hospitals. DATA SOURCES AND ANALYTIC SAMPLE: We used nationally representative administrative data on adults aged ≥ 66 years hospitalized with hip fracture from 2011 to 2019. We followed them until death or 365 days after the discharge date. PRINCIPAL FINDINGS: Across countries, the percentage of all acute-care hospitals that treated hip fractures differed widely (from 37.0% in Canada to 82.8% in Israel), with high-volume hospitals treating 4-14 times as many hip fractures as low-volume hospitals. The absolute risk-adjusted difference in 30-day mortality for high-volume compared to low-volume hospitals ranged between (-1.9% [95% CI, -2.2 to -1.7] in Canada and +1.1% [95% CI, 0.4-1.8] in the Netherlands). The proportion of patients receiving non-operative fracture treatment was lower in high-volume hospitals than low-volume hospitals in all countries (-5.4% [95% CI, -6.5 to -4.3] in Israel to -0.1% [95% CI, -0.5 to 0.3] in the Netherlands). CONCLUSIONS: Hip fracture regionalization differed substantially across countries. The direction and the magnitude of association between greater regionalization and improved patient outcomes were inconsistent across countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".