Effect of Age on Functional Outcomes After Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The incidence of stroke and the demand for stroke rehabilitation services continues to increase, and it has been proposed that age be used in combination with severity of stroke to determine type of rehabilitation. It is important to identify the impact of age on functional outcome before embracing a system that limits access to rehabilitation on the basis of age. METHODS: This prospective study includes all patients admitted to an inpatient stroke rehabilitation program for 6 years. Demographic and clinical data were extracted by means of chart review. Functional status at admission and discharge was evaluated by means of the FIM instrument. Multivariate regression techniques were used to assess the relationships between age, functional outcome, and other predictive variables. Particular attention was paid to the r(2) values to assess the amount of variation accounted for by the predictors. RESULTS: Age alone was a significant predictor of total FIM score and Motor FIM score at discharge, but not FIM change. For both total FIM score and Motor FIM score at discharge, age alone accounted for only 3% of the variation in outcome. For all the models, age explained at the most 1.3% of the variation in functional outcome after adjustment for other factors, such as admission FIM score. CONCLUSIONS: The small amount of variation that can be explained by age alone and the questionable clinical relevance of such a small effect suggest that there is no justification to deny patients access to rehabilitation solely because of advanced age.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".