Lipid accumulation product as a marker of cardiometabolic susceptibility in women with different phenotypes of polycystic ovary syndrome
Bibliographic record
Abstract
OBJECTIVE: There are limited data on cardiometabolic risk factors and the prevalence of metabolic syndrome (MetS) across the different PCOS phenotypes in Caucasian population. Lipid accumulation product (LAP) is a clinical surrogate marker that could be used for evaluation of MetS in clinical practice. The aim of the study was to analyze metabolic characteristics and the ability of LAP to predict MetS in different PCOS phenotypes. DESIGN: Cross-sectional clinical study analyzing 365 women with PCOS divided into four phenotypes according to the ESHRE/ASRM criteria, and 125 healthy BMI-matched controls. METHODS: In all subjects, LAP was determined and MetS was diagnosed according to the National Cholesterol Education Program/Adult Treatment Panel III (NCEP-ATP III), the International Diabetes Federation (IDF) and the Joint Interim Statement (JIS) criteria. Logistic regression and ROC curve analyses were used to determine predictors of MetS in each PCOS phenotype. All analyses were performed with age and BMI adjustment. RESULTS: All PCOS phenotypes in comparison to controls had higher prevalence of MetS assessed by NCEP-ATP III criteria, and only classic phenotypes when IDF and JIS criteria were used. All phenotypes had the same prevalence of MetS irrespective of used definition. LAP and exhibited the highest diagnostic accuracy and was an independent predictor of MetS in all phenotypes. CONCLUSION: LAP is an independent and accurate clinical determinant of MetS in all PCOS phenotypes in our Caucasian population. All PCOS phenotypes, including non-classic ones, are metabolically challenged and with cardiovascular risk, particularly phenotype B.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".