Converting Functional Autonomy Measurement System Scores of Patients Post-Stroke to FIM Scores
Bibliographic record
Abstract
Purpose: The Functional Independence Measure (FIM) is widely used to assess persons post-stroke. The Quebec government has selected the Functional Autonomy Measurement System (SMAF) for use in all care settings. In this article, we propose simple equations to convert SMAF scores to FIM scores for persons undergoing post-stroke rehabilitation. Method: Persons post-stroke (n=143) from three rehabilitation centres were assessed at admission and discharge using the FIM and SMAF. The sample was randomly split into derivation and validation data sets. Regression analysis was performed on the first data set to derive a conversion equation at each time point. The validity of the equations was measured using correlation coefficients, and differences between the observed and predicted FIM scores were computed from the second data set. Results: The relationship between the SMAF and FIM scores was linear at admission but quadratic at discharge. The proposed equations are, at admission, FIM=139−1.5×SMAF and, at discharge, FIM=118−0.018×SMAF2. The observed and predicted FIM scores were highly correlated in the validation data set (rs=0.92 and 0.93 at admission and discharge, respectively). Furthermore, the equations performed well in classifying stroke severity compared with a classification based on the observed FIM scores. Conclusions: SMAF scores can be reliably converted to FIM scores using the proposed equations, thus facilitating international trials in stroke rehabilitation.
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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 | 0.021 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".