Oral hormonal contraceptives and cardiovascular risks in females
Bibliographic record
Abstract
Oral hormonal contraception (OHC) is a widely employed method in females for the prevention of unintended pregnancies, as well as for the treatment of menstrual disorders, endometriosis, and polycystic ovarian syndrome. However, it is believed that with OHCs use, some females may have higher risk of cardiovascular diseases, such as hypertension, diabetes, myocardial infarction, thrombosis, and heart failure. Although such risks are infrequently detected in healthy young females with the use of oral contraceptives, slightly elevated risks of cardiovascular diseases have been observed among reproductive-aged healthy females. However, prolonged use of OHC has also been claimed to have protective cardiac effects and may contribute to reduced risk of cardiovascular disease. In fact, the debate on whether OHC administration increases the risk of cardiovascular diseases has been ongoing with inconsistent and controversial viewpoints. Nevertheless, a great deal of work has been carried out to understand the relationship between OHC use and the occurrence of cardiovascular risk in females who use OHC for preventing the unwanted pregnancy or treatment of other disorders. Therefore, in this review we summarize the most recent available evidence regarding the association between the use of oral hormonal contraceptives and the risk for cardiovascular disease in females who are using OHC to prevent unintended pregnancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".