Predicting length of stay in patients admitted to stroke rehabilitation with high levels of functional independence
Bibliographic record
Abstract
PURPOSE: In Ontario, stroke patients with relatively mild functional deficits are admitted to inpatient rehabilitation programmes. Despite apparently minor impairments, many of these patients remain in rehabilitation for prolonged periods of time. The objective of the present study is to identify variables that predict length of stay (LOS) within this population of high functioning stroke patients. METHODS: Stroke patients with Functional Independence Measure (FIM™) scores ≥100 admitted to a specialised inpatient rehabilitation programme in Ontario, Canada, from April 2005 to March 2008 were included in this study. Data from the National Rehabilitation Reporting System and chart review were used. Associations with LOS were examined among 25 potential predictor variables using bivariate correlations. Significantly correlated (p <0.002) variables were entered into a multiple linear regression. RESULTS: Twenty-four percent (n = 134) of patients admitted during the study period were identified as presenting with mild functional deficits. These patients had a mean admission age of 63.5 (SD 14.6) years, a mean admission FIM score of 109.9 (SD 6.6) and a mean LOS of 22.3 (SD 10.5) days. FIM motor and cognitive subscale scores, Mini Mental State Exam scores and five single items assessing orientation, financial independence, and verbal, written and auditory communication ability were significantly correlated with LOS. The regression model, which included all eight variables, explained 60% of the variance associated with LOS (r = 0.80). CONCLUSIONS: For patients admitted to stroke rehabilitation programmes with mild functional impairment, 60% of the variance in LOS can be explained by a small number of variables indicative of deficits in the areas of motor function, cognition and communication.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".