Medical Comorbidity and Rehabilitation Efficiency in Geriatric Inpatients
Bibliographic record
Abstract
OBJECTIVES: To measure and describe medical comorbidity in geriatric rehabilitation patients and investigate its relationship to rehabilitation efficiency. DESIGN: Prospective, multivariate, within-subject design. SETTING: The Geriatric Rehabilitation inpatient unit of the SCO Health Service in Ottawa, Canada. PARTICIPANTS: One hundred ten patients, with a mean age of 82 years. MEASUREMENTS: The rehabilitation efficiency ratio, based on gains in functional status achieved with rehabilitation treatment, and the length of stay were computed for all patients. Values were regressed on the scores of the Cumulative Illness Rating Scale (CIRS), the Mini-Mental State Examination, and the Geriatric Depression Scale to establish predictive power. RESULTS: The findings suggest that geriatric rehabilitation patients experience considerable medical comorbidity. Sixty percent of patients had impairments across six of the 13 dimensions of the CIRS, whereas 36% of patients had impairments across 11 of the 13 dimensions. In addition, medical comorbidity was negatively related to rehabilitation efficiency. This relationship was significant even after controlling for age, cognitive status, depressive symptoms, and functional independence status at admission. CONCLUSION: Medical comorbidity was a significant predictor of rehabilitation efficiency in geriatric patients. Comorbidity scores >5 were prognostic of poorer rehabilitation outcomes and can serve as an empirical guide in estimating a patient's suitability for rehabilitation. Medical comorbidity predicted both the overall functional change achieved with retabilitation (Functional Independence Measure gains) and the rate at with which those gains were reached (rehabilitation efficiency ratio).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".