Description of a methodological approach to verify the outcome-optimization of tailored therapeutic choices and test application to PCI vs. CABG.
Bibliographic record
Abstract
Background. The decision process between Percutaneous Coronary Intervention (PCI) and Bypass Graft Surgery (CABG) is based on inconclusive evidence. Yet, it is generally regarded as capable of optimizing patient outcomes. Objectives. To verify this belief through a statistical approach investigating effect modification by propensity score (PS). Methods The probability of receiving PCI as the revascularisation strategy – PS - was calculated for all the 11750 patients with severe coronary disease who underwent coronary revascularization between 2002 and 2008 in Emilia-Romagna, Italy. Long-term risks of PCI vs. CABG for death, myocardial infarction, repeat revascularization and stroke were calculated by Cox regression in each decile of PS. The homogeneity of the Hazard Ratios (HR) across deciles was assessed with a likelihood ratio test and by visual inspection. Results. Repeat revascularization was the only outcome that significantly differed across deciles of PS (p=0.05) and whose trend supported a favorable effect of the decision process. Conclusions In agreement with the current scientific uncertainty, but contrary to common opinion, the medical decision process between PCI and CABG based on demographic and clinical factors is marginally capable of optimizing the post-procedural outcomes. The proposed methodology is limited by the assumption that clinicians considered only the variables that entered the PS calculation. Keywords Outcome And Process Assessment (Health Care), Coronary Disease, Coronary Artery Bypass, Angioplasty, Patient Selection, Propensity Score
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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 | 0.158 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".