Key design elements of successful acute ischemic stroke treatment trials
Bibliographic record
Abstract
PURPOSE: We review key design elements of positive randomized controlled trials (RCTs) in acute ischemic stroke (AIS) treatment and summarize their main characteristics. METHOD: We searched Medline, Pubmed and Cochrane databases for positive RCTs in AIS treatment. Trials were included if (1) they had a randomized controlled design, with (at least partial) blinding for endpoints, (2) they tested against placebo (or on top of standard therapy in a superiority design) or against approved therapy; (3) the protocol was registered and/or published before trial termination and unblinding (if required at study commencement); (4) the primary endpoint was positive in the intention to treat analysis; and (5) the study findings led to approval of the investigational product and/or high ranked recommendations. A topical approach was used, therefore the findings were summarized as a narrative review. FINDINGS: Seventeen positive RCTs met the inclusion criteria. The majority of trials included less than 1000 patients (n = 15), had highly selective inclusion criteria (n = 16), used the modified Rankin score as a primary endpoint (n = 15) and had a frequentist design (n = 16). Trials tended to be national (n = 12), investigator-initiated and performed with public funding (n = 11). DISCUSSION: Smaller but selective trials are useful to identify efficacy in a particular subgroup of stroke patients. It may also be of advantage to limit the number of participating countries and centers to avoid heterogeneity in stroke management and bureaucratic burden. CONCLUSION: The key characteristics of positive RCTs in AIS treatment described here may assist in the design of further trials investigating a single intervention with a potentially high effect size.
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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 | 0.404 | 0.597 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.011 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".