Long-Term Follow-Up of Participants in the Taking Charge After Stroke Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The Take Charge intervention-a conversation-based, community intervention to improve motivation, improved independence, and physical health 12 months after stroke in 2 randomized controlled trials with 572 participants. This article reports long-term outcomes for the 400 participants in the TaCAS study (Taking Charge After Stroke). METHODS: Follow-up study of a New Zealand multicenter, randomized, controlled, parallel-group trial. Outcomes were collected by postal questionnaire or telephone call. The TaCAS study recruited 400 participants discharged after stroke, randomized within 16 weeks to one of 3 groups: 1 session of the Take Charge intervention, 2 sessions 6 weeks apart, or no sessions (control). This study is of participants still alive and willing to answer a questionnaire 5 to 6 years after their index stroke, undertaken in 2022. The primary outcome was the Physical Component Summary of the Short Form 36, comparing the Take Charge intervention and control. Secondary outcomes were: Frenchay Activities Index; modified Rankin Scale (mRS); survival; and stroke recurrence. These outcomes were compared with those 12 months after stroke. Analysis was by ANOVA or logistic regression. RESULTS: =0.11, both favoring Take Charge with similar point estimates to those after 12 months. Differences between Take Charge and control participants for Frenchay Activities Index scores, survival, and stroke recurrence were small and nonsignificant. CONCLUSIONS: The clinically significant improvements in physical health and independence for Take Charge participants, observed at 12 months, were sustained 5 to 6 years after stroke, but no longer statistically significant. REGISTRATION: URL: https://anzctr.org.au; Unique identifier: ACTRN12622000311752.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".