Measuring functional recovery in stroke patients: the responsiveness of ACTIVLIM-Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To determine the efficacy of the ACTIVLIM-Stroke questionnaire in detecting changes in functional status of stroke patients. METHOD: Sixty-eight Benin and Belgian stroke patients participated in this 2-month longitudinal study, involving baseline and follow-up assessments. Outcome measures combined ACTIVLIM-Stroke questionnaire, Barthel Index (BI), 6-minute-walk test (6MWT) and modified Rankin scale (MRS). Responsiveness of ACTIVLIM-Stroke was investigated through different methodological approaches and compared with BI, 6MWT and MRS. Statistical analyses were performed using the paired t tests, effect size (ES) and correlation tests. RESULTS: ACTIVLIM-Stroke detected changes in the whole sample (p<0.001, ES=0.78) and even in a subgroup of patients (p<0.001, ES=0.29) that were classified as stable according to the MRS. Moreover, ACTIVLIM-Stroke permitted the classification of patients into more discriminative groups, including those showing an important improvement (p<0.001, ES=1.87), a slight but non-clinically meaningful improvement (p<0.001, ES=0.38), and no improvement (p=0.1, ES=0.11), demonstrating its high sensitivity to change. Furthermore, there were concordant relationships between ACTIVLIM-Stroke change and any observed changes in BI, 6MWT and MRS scores (r≥0.50, p<0.001), confirming the external responsiveness of ACTVLIM-Stroke. CONCLUSIONS: ACTIVLIM-Stroke showed good responsiveness and can detect accurately clinical changes in the functional status of stroke patients. The BI and the 6MWT were also responsive and may provide complementary information while investigating change in functional status. However, in addition to being highly sensitive to change, ACTIVLIM-Stroke presents significant methodological advantages for quantifying functional changes in stroke patients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".