Determinants and outcomes of inpatient versus home based rehabilitation following elective hip and knee replacement.
Bibliographic record
Abstract
OBJECTIVE: There are large variations in practice patterns and costs of rehabilitation following total joint replacement (TJR). We evaluated the determinants of rehabilitation setting (home based vs inpatient) after TJR, and its influence on early functional outcomes. METHODS: We studied a retrospective cohort of 146 primary total hip and knee replacements. Ninety-six patients completed a mailed survey consisting of the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), the Medical Outcomes Survey Short Form-36 (SF-36), and a satisfaction questionnaire. RESULTS: The mean age of the cohort was 66 years, 70% were women, and osteoarthritis was the primary diagnosis in 79%. Thirty-nine percent received home based rehabilitation. Determinants of home based rehabilitation included preference for home based rehabilitation, male sex, and knowledge regarding TJR. At a mean followup of 8 months post TJR, there were no significant differences between the inpatient and home based rehabilitation groups with respect to the WOMAC, SF-36, and satisfaction scores. CONCLUSION. These results support continued use of home based rehabilitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".