Prognosis in 10 years of follow-up of three therapeutic strategies for chronic multivessel coronary artery disease in women (study MASS)
Bibliographic record
Abstract
Studies were contradictory regarding increased mortality in women undergoing coronary interventions. The worst prognosis in women was justified by aging, body mass index and more comorbidity. The aim of this study was to analyze the long-term prognosis of each therapeutic strategy, Coronary Artery Bypass Graft (CABG), Percutaneous Coronary Intervention (PCI) and Medical Treatment (MT) in men and women. Methods: A prospective 10-year follow-up study randomized 1084 patients with stable chronic CAD for MT (N=324, 30%), PCI (N=306, 28%) or CABG (N=454, 42%). The number of women for each strategy was respectively: 100 (9%), 97 (9%) and 116 (10%). CAD was defined by the presence of angina pectoris CCS class II and III, positive exercise stress testing, ejection fraction >40% and ≥2 coronary lesions >70%. Primary outcomes were incidence of total mortality, Q-MI, or refractory angina that required revascularization. All data were analyzed according to the intention-to-treat principle. Results: Women had a higher number of primary events with the CABG strategy (p=0.002) and similar to the MT (p=0.902) and ICP (p=0.465), however the CABG was the best therapeutic strategy in women (Figure 1). To death, no differences were observed for both sexes in all strategies (Figure 2). In multivariate Cox regression sex was not an independent variable for each strategy for both primary events and death. For primary events independent variables for poor outcome for each strategy were: PCI, diabetes (p = 0.030); MT, hypertension (p=0.006); CABG, age (p<0.001). To death, were: PCI, no variable; MT, age (p=0.011) and diabetes (p=0.030); CABG, age (p<0.001). Conclusion: CABG was the best treatment strategy in women despite the higher number of primary events compared with men.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".