Relationship Between Acute Care Hospital-Based Factors and Discharge Destination for Rehabilitation Following a Hip Fracture
Bibliographic record
Abstract
ABSTRACT Hospitals may transfer seniors with a hip fracture to various rehabilitation settings. Knowing the relationship between hospital teaching status and post-acute rehabilitation setting may help evaluations of the transfer from acute care. The purpose of this study was to determine the relationship between hospital teaching status and rehabilitation destination following acute care in seniors with a hip fracture. Hospital separations were linked with home care records to identify hip fractures and hospital-based or home care rehabilitation (n = 806). Two logistic regression models determined the likelihood of transfer to any rehabilitation destination and to hospital-based versus home care rehabilitation. Teaching hospitals were no more likely than non-teaching hospitals to discharge patients to any rehabilitation (OR 1.20, 95% CI 0.88,1.65). However, among those referred to rehabilitation, the odds of discharge to hospital-based versus home care rehabilitation were almost four times greater for patients in teaching hospitals (OR 3.76, 95% CI 2.23, 6.37). The results are consistent with the availability of post-acute rehabilitation in the planning area. Future study of post-acute rehabilitation outcomes should consider hospital teaching status as an indicator of how hospital-based factors may affect the utilization of post-acute rehabilitation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".