Distribution of HDL-C and LDL-C and Their Subfractions in Mammary Carcinogenesis. Case-Control Study
Bibliographic record
Abstract
RESULTS: The cohort comprised 1,992,972 women (33,064 with SMM).Incidence rates for subsequent CVD hospitalization were 26.4 and 11.2 per 10,000 person-years in SMMaffected and SMM-unaffected women, respectively.The strongest pregnancy-related predictor of CVD was hypertensive SMM (adjusted hazard ratio [aHR], 2.28; 95% CI, 1.79-2.89),whereas the strongest nonpregnancy predictor was > 1 antecedent comorbid cardiovascular risk factor (aHR, 4.95 [4.57-5.36]).LRs for CVD within 1 year were 0.57 (0.51-0.64), 1.43 (1.29-1.58),and 6.79 (5.76-8.01)for standardized risk scores of < 0, 0 to < 3, and ! 3, respectively.Optimism-corrected C-statistics for CVD at 1, 5, and 10 years were 0.68 (0.68-0.69), 0.71 (0.71-0.71), and 0.75 (0.75-0.75), respectively.CONCLUSIONS: By incorporating SMM and various factors, a novel risk score might aid in identifying postpartum women at risk for CVD.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".