How can we improve targeting of frail elderly patients to a geriatric day-hospital rehabilitation program?
Bibliographic record
Abstract
BACKGROUND: The optimal patient selection of frail elderly persons undergoing rehabilitation in Geriatric Day Hospital (GDH) programs remains uncertain. This study was done to identify potential predictors of rehabilitation outcomes for these patients. METHODS: This study is a retrospective cohort analysis of patients admitted to the rehabilitation program of our GDH, in Montreal, Canada, over a five year period. The measures considered were: Barthel Index, Older Americans Resources and Services, Folstein Mini Mental Status Exam, Timed Up & Go (TUG), 6-minute walk test (6 MWT), Gait speed, Berg Balance, grip strength and the European Quality of life - 5 Dimensions. Successful improvement with rehabilitation was defined as improvement in three or more tests of physical function. Logistic regression analysis using the Bayesian Information Criterion (BIC) was employed to select the optimal model for making predictions of rehabilitation success. RESULTS: A total of 335 patients were studied, but only 233 patients had a complete data set suitable for the predictive model. Average age was 81 years and patients attended the GDH an average of 24 visits. Significant changes were found in several measures of physical performance for many patients ranging from improved gait speed in 21.3% to improved TUG in 62.7% of the cohort. Fifty-eight percent of patients attained successful improvement with rehabilitation by our criteria. This group was characterized by lower test scores on admission. Using BIC, the best predictor model was the 6 MWT [OR: 0.994 per meter walked (95% CI: 0.990-0.997)]. CONCLUSIONS: The GDH rehabilitation program is effective in improving patients' physical performance. Although no single measure was found to be sufficiently predictive to help target candidates appropriately, the 6 MWT showed a trend to significance. Further research will be done to elucidate the utility of a composite 'rehab appropriateness index' and the role of International Classification of Function concepts for targeting frail elderly to GDH rehabilitation services.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".