Long term follow-up of participants in the Taking Charge after Stroke randomised controlled trial
Bibliographic record
Abstract
ABSTRACT Background The Take Charge intervention – a conversation-based, community intervention to improve motivation, improved independence and physical health 12 months after stroke in two randomised controlled trials with 572 participants. This paper reports long term outcomes for the 400 participants in the Taking Charge After Stroke (TaCAS) study. Method A follow-up study, of a multicentre, randomised, controlled, parallel-group trial. Outcome measures were collected by postal questionnaire or by telephone call. The TaCAS study recruited 400 participants discharged after stroke, randomised within 16 weeks to one of three groups: one session of the Take Charge intervention, two sessions six weeks apart or no sessions (control). This study is of participants still alive and willing to answer a questionnaire between 5 and 6 years after their index stroke. The primary outcome was the Physical Component Summary (PCS) of the Short Form 36. Secondary outcomes were: Frenchay Activities Index; modified Rankin Score (mRS); survival; and stroke recurrence. Results Mortality data were available for all 400 participants and functional data for 204/296 (69%) of survivors. The mean difference (95% CI) in PCS between Take Charge and control groups was 2.8 (−0.8 to 6.5) units, p = 0.12, and for independence (mRS 0-2) the odds ratio (95% CI) was 0.56 (0.28 to 1.16) p = 0.11, both favouring Take Charge with similar point estimates to those after 12 months. Point estimates for other outcomes also favoured Take Charge but were not statistically significant. Conclusions Differences in physical health and independence observed at 12 months, were sustained 5-6 years after stroke, but were not statistically significant.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".