Stroke, rehabilitation, triage, discharge destinations, stroke severity, Functional Autonomy Measurement System (SMAF)
Bibliographic record
Abstract
Background: Patients 3-5 days post stroke should be evaluated with a toolkit of valid assessment tools to assist in rehabilitation destination planning. A tool to assess stroke severity is a key component. A short form of the Functional Independence Measure (FIM), the AlphaFIM, is widely used. In Quebec, the Functional Autonomy Measurement System (SMAF) is used, however no short version exists.Objective: To develop a short version of the SMAF, the SF-SMAF, to assist in selecting the optimal rehabilitation destination.Methods: Patients, on average 5 days post stroke (n = 207) admitted to four academic acute hospitals were assessed with the SMAF. Linear regressions with the SMAF total score as the dependent variable and its 29 items as independent variables identified subgroups of items for inclusion in the SF-SMAF. A panel of experts (n = 10) selected the best subgroup of items, based on clinical experience. Multinomial logistic regression was used to examine the ability of the SF-SMAF to predict discharge destinations (home without rehabilitation, outpatient or inpatient rehabilitation). Results: Regression analyses and the expert panel led to retaining 4 of the 29 items of the SMAF: washing, walking inside, judgment, budgeting (R2 = 94%). The SF-SMAF predicts 66% of the rehabilitation discharge destinations. Adding the variables u201cpresence of barriers to returning homeu201d and u201ccommunicationu201d to the SF-SMAF raise the prediction to 81%.Conclusion: The SF-SMAF estimates the SMAF, is quick and easy to administer, and provides a measure of stroke severity to inform the choice of optimal rehabilitation destination.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".