Multiple approaches to assessing the effects of delays for hip fracture patients in the United States and Canada.
Bibliographic record
Abstract
OBJECTIVE: To examine the determinants of postsurgery length of stay (LOS) and inpatient mortality in the United States (California and Massachusetts) and Canada (Manitoba and Quebec). DATA SOURCES/STUDY SETTING: Patient discharge abstracts from the Agency for Health Care Policy and Research Nationwide Inpatient Sample and from provincial health ministries. STUDY DESIGN: Descriptive statistics by state or province, pooled competing risks hazards models (which control for censoring of LOS and inpatient mortality data), and instrumental variables (which control for confounding in observational data) were used to analyze the effect of wait time for hip fracture surgery on postsurgery outcomes. DATA EXTRACTIONS: Data were extracted for patients admitted to an acute care hospital with a primary diagnosis of hip fracture who received hip fracture surgery, were admitted from home or the emergency room, were age 45 or older, stayed in the hospital 365 days or less, and were not trauma patients. PRINCIPAL FINDINGS: The descriptive data indicate that wait times for surgery are longer in the two Canadian provinces than in the two U.S. states. Canadians also have longer postsurgery LOS and higher inpatient mortality. Yet the competing risks hazards model indicates that the effect of wait time on postsurgery LOS is small in magnitude. Instrumental variables analysis reveals that wait time for surgery is not a significant predictor of postsurgery length of stay. The hazards model reveals significant differences in mortality across regions. However, both the regressions and the instrumental variables indicate that these differences are not attributable to wait time for surgery. CONCLUSIONS: Statistical models that account for censoring and confounding yield conclusions that differ from those implied by descriptive statistics in administrative data. Longer wait time for hip fracture surgery does not explain the difference in postsurgery outcomes 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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".