Discharge destination of individuals with severe stroke undergoing rehabilitation: a predictive model
Bibliographic record
Abstract
PURPOSE: To determine the predictive value of age, stroke severity and caregiver availability for discharge destination of individuals with severe stroke following inpatient rehabilitation. METHODS: Telephone interviews were conducted with individuals recovering from a severe stroke (Rehabilitation Patient Groups 1100 and 1110), who were discharged from a specialized inpatient stroke rehabilitation unit between April 2005 and December 2009, to determine caregiver availability at the time of discharge. Age and Functional Independence Measure (FIM®) scores were obtained retrospectively by chart review. These three variables were analyzed using logistic regression as potential predictors of discharge destination (home versus long-term care). RESULTS: Data from 189 individuals were analyzed. Younger age, higher admission FIM® scores and the presence of a willing and able caregiver were significantly associated with discharge home. Only one individual was discharged home in the absence of a caregiver. CONCLUSION: Age, admission FIM® and caregiver availability were significant predictors of post-rehabilitation discharge destination. Results of this study suggest that for individuals with severe stroke, discharge home is unlikely in the absence of an available caregiver. Implications for Rehabilitation Age, stroke severity at admission and the availability of a caregiver play an important role in determining post rehabilitation discharge destination of individuals with severe stroke. These factors must be considered to assist with appropriate goal setting and discharge planning during inpatient rehabilitation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".