Predictive factors for home discharge after femoral fracture surgery: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Femoral fractures require protracted hospitalization and often preclude return to pre-fracture levels of mobility, function and prior residential status following hospital discharge. Early prediction of rehabilitation and discharge potential in patients with femoral fracture would optimize discharge planning. AIM: To identify predictive factors of discharge destination during the early phase of femoral fracture rehabilitation. DESIGN: Prospective cohort design. SETTING: Acute and postoperative rehabilitation hospital settings. POPULATION: Data from 109 participants (65 women [59.6%]) admitted for unilateral femoral fracture were included. METHODS: Sociodemographic information, hip pain severity during gait (Numeric Pain Rating Scale), mobility (Elderly Mobility Scale), activities of daily living (Modified Barthel Index), cognition (Mini-Mental State Examination [MMSE]), exercise self-efficacy (Self-Efficacy for Exercise Scale), amount of physiotherapy received, and caregiver availability were assessed pre- and/or postoperatively. Discharge destination was assessed via telephone interviews 6 weeks after discharge from acute care. Receiver operating characteristic curves were used to determine optimal cut-off scores for all outcomes based on discharge destination. Outcomes demonstrating a significant area under the curve were entered as dichotomous independent variables (i.e., above or below ROC-derived cut-off values) in subsequent logistic regression analyses to determine predictors of discharge destination. RESULTS: SEE Score ≥53 (odds ratio [OR]=5.975, 95% confidence interval [CI]=1.674-21.333, P=0.006), female sex (OR=3.421, 95% CI=1.187-9.861, P=0.023), ≥8 physiotherapy sessions (OR=4.633, 95% CI=1.559-13.771, P=0.006), MMSE Score ≥17 (OR=3.374, 95% CI=1.047-10.873, P=0.042), and caregiver availability (OR=3.766, 95% CI=1.133-12.520, P=0.030) were identified as significant predictors of home discharge. CONCLUSIONS: Exercise self-efficacy, female sex, more physiotherapy rehabilitation training, better pre-operative cognitive function, and caregiver availability emerged as important predictors of home discharge following femoral fracture. CLINICAL REHABILITATION IMPACT: These findings are highly translational and may be useful for informing clinical guidelines and policy decisions regarding rehabilitation potential and discharge pathway selection during early hospitalization following femoral fracture surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".