Network meta‐analysis can inform the ethical evaluation of trials that randomise away from standard of care: The case of symptomatic carotid stenosis
Bibliographic record
Abstract
OBJECTIVE: Little guidance exists on the conduct of randomised clinical trials (RCT) that seek to randomise patients away from standard of care. We sought to test the technique of network meta-analysis (NMA) to ascertain best available evidence for the purposes of informing the ethical evaluation of RCTs under these circumstances. We used the example of RCTs for patients with symptomatic, moderate to severe carotid stenosis that seek to compare surgical intervention plus medical therapy (standard of care) versus medical therapy (less than standard of care). STUDY DESIGN AND SETTING: Network meta-analysis of RCTs of adults with symptomatic carotid artery stenosis of 50%-99% who were treated with carotid endarterectomy (CEA), carotid artery stenting (CAS), or medical therapy (MT). The primary outcome was any stroke or death until end of follow-up, and secondary outcome was 30-day risk of ipsilateral stroke/death. RESULTS: We analysed eight studies, with 7187 subjects with symptomatic moderate/severe stenosis (50%-99%). CEA was more efficacious than MT (HR = 0.82, 95% credible intervals [95% CrI] = 0.73-0.92) and CAS (HR 0.73, 95% CrI = 0.62-0.85) for the prevention of any stroke/death. At 30 days, the odds of experiencing an ipsilateral stroke/death were significantly lower in the CEA group compared to both MT (OR = 0.58, 95% CrI = 0.47-0.72) and CAS (OR = 0.68, 95% CrI = 0.55-0.83). CONCLUSION: Our results support the feasibility of using NMA to assess best available evidence to inform the ethical evaluation of RCTs seeking to randomise patients away from standard of care. Our results suggest that a strong argument is required to ethically justify the conduct of RCTs that seek to randomise patients away from standard of care in the setting of symptomatic moderate to severe carotid stenosis.
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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 | 0.450 | 0.751 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".