Assessment of Sex Differences in Plaque Morphology by Coronary Computed Tomography Angiography—Are Men and Women the Same?
Bibliographic record
Abstract
PURPOSE: The objective of this study was to assess whether sex differences exist in plaque burden and plaque subtype as assessed by coronary computed tomography angiography (CCTA). METHODS: The study cohort included 937 consecutive patients who underwent CCTA between 2008 and 2010. Stenosis was quantified using the Society of Cardiovascular Computed Tomography stenosis grading scale and a total stenosis score (TSS) was generated. Plaque morphology (PM) was reported as predominantly calcified (CP), noncalcified (NCP), or mixed (MP) plaque, and CP, NCP, and MP percentages were calculated. RESULTS: On multivariate analysis, men were significantly more likely to have plaque (65.9% of men vs. 44.6% of women, p<0.001), at least one segment with ≥50% stenosis (22.7% of men vs. 10.3% of women, p<0.001) and higher TSS (mean score=2.81 for men vs. 1.58 for women, p<0.001). Sex was the strongest predictor in all models (odds ratio [OR]=2.55, 95% confidence interval [CI] 1.78-3.67, p<0.001 for any plaque; OR=2.48, 95% CI 1.48-4.16, p<0.01 for segments with ≥50% stenosis; β=1.46, 95% CI 0.69-2.22, p<0.001 for TSS). Among patients with coronary plaque present, no significant sex differences in PM were found. CONCLUSIONS: Sex was the strongest risk factor for the presence and extent of plaque. Significant sex differences in PM did not exist.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".