On the pooling and subgrouping of data from percutaneous coronary intervention versus coronary artery bypass grafting trials: a call to circumspection
Bibliographic record
Abstract
In the modern era, treatment choice is guided by scientific evidence, usually gathered from well-conducted clinical trials, and often followed by the pooling of their data. In this article, we review the most recent pooled evidence regarding myocardial revascularization strategies and discuss how these meta-analyses have inherent shortcomings that should be better understood, prior to their purported conclusions potentially influencing clinical decisions. \n \nProperly conducted randomized trials comparing percutaneous coronary intervention (PCI) with coronary artery bypass grafting (CABG) can provide unbiased estimates of treatment effects. However, the design of such trials has often involved a primary outcome that is a composite measure and thus open to challenges with regard to the appropriate interpretation of each individual components. Quantitative synthesis of such data from multiple trials can enable estimates of individual components of the composite outcomes (e.g. all-cause mortality). Where individual patient data are available, a full investigation of mediating effects, or subgroups analyses, may also be undertaken. Relevant to this article, we now have multiple meta-analyses of trials available, which provide an opportunity to assess the appropriateness of criteria for patient selection between PCI and CABG. Consequently, we will appraise the robustness of these meta-analytic methods in answering the question at stake: does PCI provide equivalent results to CABG for the treatment of unprotected left main coronary artery stenosis and multivessel coronary artery disease?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.598 | 0.682 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".