Sex differences in plaque characteristics of fractional flow reserve-negative non-culprit lesions after myocardial infarction
Bibliographic record
Abstract
Abstract Background and aims Recurrent events after myocardial infarction (MI) are common and often originate from native non-culprit (NC) lesions that are non-flow limiting. These lesions consequently pose as targets to improve long-term outcome. It is, however, largely unknown whether these lesions differ between sexes. The aim of this study was to assess such potential differences. Methods From the PECTUS-obs study, we assessed sex-related differences in plaque characteristics of fractional flow reserve (FFR)-negative intermediate NC lesions in 420 MI-patients. Results Among the included patients, 80 (19.1 %) were female and 340 (80.9 %) male. Women were older and more frequently had hypertension and diabetes. In total, 494 NC lesions were analyzed. After adjustment for clinical characteristics and accounting for within-patients clustering, lesion length was longer in female patients (20.8 ± 10.0 vs 18.3 ± 8.5 mm, p = 0.048) and minimum lumen area (2.30 ± 1.42 vs 2.78 ± 1.54 mm2, p < 0.001) and minimum lumen diameter (1.39 ± 0.45 vs 1.54 ± 0.44 mm, p < 0.001) were smaller. The minimum fibrous cap thickness was smaller among females (96 ± 53 vs 112 ± 72 μm, p = 0.025), with more lesions harboring a thin cap fibroatheroma (39.3 % vs 24.9 %, p < 0.001). Major adverse cardiovascular events at two years occurred in 6.3 % of female patients and 11.8 % of male patients (p = 0.15). Conclusions FFR-negative NC lesions after MI harbored more high-risk plaque features in female patients. Although this did not translate into an excess of recurrent events in female patients in this modestly sized cohort, it remains to be investigated whether this difference affects clinical 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".