Abstract WMP48: The Credibility Of Subgroup Analyses Reporting In Stroke Trials Is Poor: A Methodological Review.
Bibliographic record
Abstract
Background: Subgroup analyses are widely used to evaluate heterogeneity of treatment effects in randomized clinical trials. However, there is a limited investigation of the quality of prespecified and reported subgroup analyses in stroke trials. This study evaluated the credibility of subgroup analyses in stroke trials. Methods and Analysis: We searched Medline/PubMed, Embase, the Cochrane Central Register of Controlled Trials, and the Web of Science from inception to March 24 th , 2021. Three reviewers screened, extracted, and analyzed the data from the publications. Primary publications of stroke trials that reported at least one subgroup effect and had published corresponding study protocols were included. The Instrument for Assessing the Credibility of Effect Modification Analyses (ICEMAN) was used to examine the quality of the subgroup effects reported. The risk of bias was assessed using the Cochrane Risk-of-Bias tool for randomized trials version 2. Results: Seventy-four articles met the inclusion criteria and altogether reported 647 subgroup effects. The median sample size was 1264 (interquartile range (IQR): 380 - 3876) while the median number of subgroups prespecified in the protocol was 6 (IQR:2 - 10), and 61 studies (82.4%) used the univariate test of interaction. One hundred and thirty-nine subgroup effects (43.6%) in acute stroke treatment and 131 subgroup effects (35.03%) in studies published in 2015 or later had moderate credibility. Overall, 458 subgroup effects (70.8%) had low credibility, while 189 subgroup effects (29.2%) had moderate credibility. Conclusion: Subgroup analysis reporting quality in stroke trials remains poor. Trialists and medical journal publishers must ensure that reporting guidelines, such as ICEMAN, are adopted to improve the credibility of reported subgroup analyses in stroke trials.
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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: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.313 | 0.692 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".