Mediation analysis to inform policy on coronary revascularization by expected time to treatment: Analytical framework
Bibliographic record
Abstract
ABSTRACT Objectives Clinical guidelines favour coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) for patients with stable complex coronary disease. Yet the benefit of CABG as established in trials may not be generalizable to populations in which treatment method determines time to treatment, typically being longer for CABG. For cases in which the cardiac anatomy is suitable for either treatment, it is unclear whether it is appropriate to recommend CABG, which is likely to be delayed, if PCI can be performed sooner. This paper outlines an analytical framework for a policy analysis of the timing of coronary revascularization. Methods We constructed a thought experiment to examine whether time to treatment will influence the advantage of CABG. We substantiated the use of mediation analysis to estimate the extent to which differences in outcomes between CABG and PCI would change if times to CABG were the same as times to PCI. Results We designed a study that uses data from a population-based patient registry to obtain effect measures of mediation analysis: the total effect, the natural indirect effect, and the natural direct effect. The partitioning of the total effect will allow us to estimate the proportional reduction in the risk of an outcome if the time to CABG was similar to that of PCI. Interpretation Treatment recommendation, resource allocation and scheduling benchmarks will be guided by understanding the extent to which the time to treatment mediates the relation between revascularization method and outcome.
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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 | 0.126 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".