Activity and participation in stroke survivors in a low‐income setting: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVES: To describe patients' activity and participation levels and to compare these levels across different groups of stroke survivors according to their walking speed (WS). METHODS: In this cross-sectional study, 67 stroke survivors (43 men, mean age: 58.4 ± 12.9 years old) were assessed using the stroke impairment assessment set (SIAS), ACTIVLIM-Stroke, 10-m walk test (10MWT), 6-min walk test (6MWT) and Reintegration to Normal Living Index (RNLI). The sample was afterwards split into three WS sub-groups (<0.4 m/s, 0.4-0.8 m/s and >0.8 m/s) based on 10MWT scores. RESULTS: ACTIVLIM-Stroke, 10MWT and 6MWT mean scores (±SD) were, respectively, 69.4 ± 20.2%, 0.9 ± 0.6 m/s and 282.1 ± 182 m. RNLI median score (range) was 5 (0-20). Sub-group analyses indicated that 26.9% (n = 18) obtained WS < 0.4 m/s, 13.4% (n = 9) WS between 0.4 and 0.8 m/s, and 59.7% (n = 40) WS > 0.8 m/s. Significant differences (p < .001) were found between WS sub-groups for both activity and participation. CONCLUSION: Stroke survivors in Kinshasa presented a good performance for basic-activities of daily life (basic-ADLs). However, some of them still had difficulties with some community activities. Differences in WS seemed to discriminate well stroke survivors in terms of activity and participation, since the higher WS, the more they performed in basic-ADLs, walking distance and participation, and inversely.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".