Clinical Utility of the AlphaFIM® Instrument in Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND: The AlphaFIM instrument is an assessment tool designed to facilitate discharge planning of stroke patients from acute care, by extrapolating overall functional status from performance in six key Functional Independence Measure (FIM) instrument items. AIM: To determine whether acute care AlphaFIM rating is correlated to stroke rehabilitation outcomes. METHODS: In this prospective observational study, data were analyzed from 891 patients referred for inpatient stroke rehabilitation through an Internet-based referral system. Simple linear and stepwise regression models determined correlations between rehabilitation-ready AlphaFIM rating and rehabilitation outcomes (admission and discharge FIM ratings, FIM gain, FIM efficiency, and length of stay). Covariates including demographic data, stroke characteristics, medical history, cognitive deficits, and activity tolerance were included in the stepwise regressions. RESULTS: The AlphaFIM instrument was significant in predicting admission and discharge FIM ratings at rehabilitation (adjusted R² 0.40 and 0.28, respectively; P < 0.0001) and was weakly correlated with FIM gain and length of stay (adjusted R² 0.04 and 0.09, respectively; P < 0.0001), but not FIM efficiency. AlphaFIM rating was inversely related to FIM gain. Age, bowel incontinence, left hemiparesis, and previous infarcts were negative predictors of discharge FIM rating on stepwise regression. Intact executive function and physical activity tolerance of 30 to 60 mins were predictors of FIM gain. CONCLUSIONS: The AlphaFIM instrument is a valuable tool for triaging stroke patients from acute care to rehabilitation and predicts functional status at discharge from rehabilitation. Patients with low AlphaFIM ratings have the potential to make significant functional gains and should not be denied admission to inpatient rehabilitation programs.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".