A methodological review of pragmatic designs in acute stroke trials
Bibliographic record
Abstract
BACKGROUND: Randomized controlled trials (RCTs) have traditionally been designed with an explanatory approach, in contrast to incorporating real-world, pragmatic considerations. AIMS: This methodological review assesses the uptake of pragmatic designs in Phase III acute stroke RCTs. METHODS: We conducted a comprehensive literature search of the MEDLINE, Embase, and Cochrane Library databases from inception to 1 July 2024. Eligible articles included English-language published Phase III RCTs of acute ischemic stroke and intracerebral hemorrhage interventions. Using the Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2) tool, each trial was rated on nine key domains, and relevant study characteristics were extracted. Trials with an average rating of 3 or higher, or a total score (sum of ratings) of 27 or higher (given that all domains were assessed), were considered to adopt an overall pragmatic approach to their design. Risk of bias was evaluated using the Cochrane risk of bias tool. RESULTS: Of the 5663 unique articles obtained after deduplication, 136 trials were included, and 71 (52%) trials were classified as pragmatic using the PRECIS-2 tool. A majority had a low risk of bias (63.2%). Pragmatic trials were more likely to be large sample, multicenter, multinational trials with broad inclusion criteria that cover multiple types of strokes. CONCLUSION: There has been an increased uptake of pragmatic designs in acute stroke over the last decade, reflecting improvements in acute stroke care and a greater consideration of real-world applicability by trialists.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.471 | 0.779 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".