LBODP026 Early Atherosclerosis In High-risk Women With Pcos
Bibliographic record
Abstract
Abstract Background Polycystic Ovary Syndrome (PCOS) is associated with increased cardiometabolic risk factors and cardiovascular disease (CVD). Early CVD screening may be an important tool in young high-risk women to effectively prevent premature morbidity from CVD. Objective The aim of this study was to provide evidence-based research in the screening and assessment of early atherogenic dyslipidemia, cardiac dysfunction and subclinical atherosclerotic CVD (ACVD) in high-risk women with and without PCOS. Design, Setting, participants: A case-control study in high-risk women aged 25-45 years with (n=45) and without PCOS (n=25), matched for age and body mass index. Main outcome measures: Atherogenic dyslipidemia including a standard lipid panel, total apoB and remnant cholesterol were measured. ACVD was measured using carotid intima-media thickness (cIMT) and presence of carotid plaque, and cardiac function using ultrasound and 2D/3D echocardiography. Results Fasting plasma triglycerides (1.9 ± 0.2 vs 1.2 ± 0.2 mM)and remnant cholesterol (0.85 ± 0.1 vs 0.6 ± 0.1 mM) were significantly elevated by 30% and total apoB tended to be elevated by 20% (1. 0 ± 0.1 vs 0.8 ± 0.1 g/L) in PCOS compared to controls. Those with PCOS had a 5-fold higher incidence of carotid plaque compared to controls but no difference in cIMT or cardiac function indices were observed. Conclusion Our data shows detection of early ACVD in PCOS compared to controls, and this is associated with atherogenic dyslipidemia. These results suggest early screening and a risk-stratification model may be warranted in high-risk young women with and without PCOS. Presentation: No date and time listed
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".