Relationship between frailty and discharge outcomes in subacute care
Bibliographic record
Abstract
AIMS: To determine whether level of frailty can predict length of stay, discharge destination, level of participation in physiotherapy, and degree of physical improvement with physiotherapy in older, subacute hospital patients. METHOD: The Edmonton Frail Scale (EFS) was administered to 75 older people in a subacute hospital setting. Relationships between EFS score and a range of other measures, including participation in physiotherapy, Elderly Mobility Scale, discharge destination and length of stay, were examined. RESULTS: Level of frailty did not predict length of stay (rho=-0.13, P=0.24), discharge destination (t=-1.32, P=0.19), raw change on the Elderly Mobility Scale (rho=0.06, P=0.61) or rate of change on the Elderly Mobility Scale (r=-0.001, P=0.98). In addition, participants with a high level of frailty were more likely to achieve a satisfactory level of participation in physiotherapy sessions than those with low frailty (OR 1.43, P=0.02). CONCLUSION: Level of frailty measured with the EFS was not a useful predictor of rehabilitation and discharge outcomes for older people in subacute care. These results do not support the routine use of the EFS to measure frailty in subacute care. WHAT IS KNOWN ABOUT THIS TOPIC? In a community-dwelling population, level of frailty has been found to predict poor outcomes from surgery, falls, fractures, disability, need for residential care and mortality. However, little is known about the impacts of frailty in a subacute setting, nor how frailty could best be measured in this setting. WHAT DOES THIS PAPER ADD? The use of the EFS as a predictive tool was not supported by the results of this exploratory study. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS? Alternative frailty measures may be more suitable than the EFS for patients in a subacute setting.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".