Prediction of functional outcomes in stroke inpatients receiving rehabilitation.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early identification of predictive factors relevant to functional outcomes for stroke patients is important to the establishment of an effective continuing care program. The objective of this study was to identify the predictive factors related to functional outcome at discharge after stroke rehabilitation therapy. METHODS: 105 first-time stroke patients admitted to the inpatient rehabilitation department of a university-based medical center were recruited for this prospective study. The functional outcomes of the patients were assessed at admission and at discharge using the Functional Independence Measure (FIM). Severity of stroke was determined using the Canadian Neurological Scale (CNS). Age, gender, side of hemiplegia (SIDE), type of stroke (TYPE), onset to admission interval (OAI), and length of rehabilitation stay (LORS) were also included as predictor variables. RESULTS: The mean (+/- SD) FIM score at discharge (76.6 +/- 26.4) correlated strongly (r = 0.78, p < 0.001) with the admission FIM score (56.3 +/- 24.1), moderately (r = 0.46, p < 0.001) with the admission CNS score (6.1 +/- 2.2), negatively (r = -0.38, p < 0.001) with age (63.2 +/- 12.3 years), negatively (r = -0.26, p = 0.009) with OAI (24.2 +/- 16.0 days), and negatively (r = -0.29, p = 0.002) with LORS (34.7 +/- 16.8 days). Stepwise regression analyses indicated that admission FIM score, age, and admission CNS score were the strongest predictors of functional outcome and accounted for 66% of the total variation in discharge FIM total score. The admission FIM score was the best predictor and accounted for 61% of the variation. CONCLUSIONS: The findings of this study imply that the admission FIM scores for inpatients receiving stroke rehabilitation can be used to predict functional outcomes at discharge from hospital.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".