Rationale for Intervention and Dose Is Lacking in Stroke Recovery Trials: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The ineffectiveness of most complex stroke recovery trials may be explained by inadequate intervention design. The primary aim of this review was to explore the rationales given for interventions and dose in stroke rehabilitation randomised controlled trials (RCTs). METHODS: We searched the Cochrane Stroke Group library for RCTs that met the following criteria: (1) training based intervention; (2) >50% participants who were stroke survivors; (3) full peer-reviewed text; (4) English language. We extracted data on 16 quality items covering intervention dose (n= 3), trial design (n= 10), and risk of bias (n= 3) and 18 items related to trial method. Logistic regression analyses were performed to determine whether (1) reporting of trial quality items changed over time; (2) reporting of quality items was associated with the likelihood of a positive trial, adjusted for sample size and number of outcomes. RESULTS: 27 Cochrane reviews were included, containing 9,044 participants from 194 trials. Publication dates were 1979 to 2013, sample size was median 32 (IQR 20,58), and primary outcome was reported in 49 trials (25%). The median total quality score was 4 (IQR 3,6) and improved significantly each year (OR 1.12, 95% CI 1.07, 1.16, p<0.001). Total quality score was not associated with likelihood of a positive trial, but trials containing a biological rationale for the intervention were more likely to find a difference in patient outcome (OR 2.18, 95% CI 1.14, 4.19, p=0.02). CONCLUSION: To develop breakthrough treatments we need to build the rationale for research interventions and testing of intervention dosage. This will be achieved through a collective research agenda to understand the mechanistic principles that drive recovery and identification of clearer targets for clinical trials.
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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 | 0.447 | 0.747 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".