Hypertensive Disorders of Pregnancy and Medication Use in the 2015 Pelotas (Brazil) Birth Cohort Study
Bibliographic record
Abstract
Hypertensive disorders of pregnancy account for approximately 22% of all maternal deaths in Latin America and the Caribbean. Pharmacotherapies play an important role in preventing and reducing the occurrence of adverse outcomes. However, the patterns of medications used for treating women with hypertensive disorders of pregnancy (HDP) living in this country is unclear. A population-based birth cohort study including 4262 women was conducted to describe the pattern of use of cardiovascular agents and acetylsalicylic acid between women with and without HDP in the 2015 Pelotas (Brazil) Birth Cohort. The prevalence of maternal and perinatal outcomes in this population was also assessed. HDP were classified according to Ministry of Health recommendations. Medications were defined using the Anatomical Therapeutic Chemical Classification System and the substance name. In this cohort, 1336 (31.3%) of women had HDP. Gestational hypertension was present in 636 (47.6%) women, 409 (30.6%) had chronic hypertension, 191 (14.3%) pre-eclampsia, and 89 (6.7%) pre-eclampsia superimposed on chronic hypertension. Approximately 70% of women with HDP reported not using any cardiovascular medications. Methyldopa in monotherapy was the most frequent treatment (16%), regardless of the type of HDP. Omega-3 was the medication most frequently reported by women without HDP. Preterm delivery, caesarean section, low birth weight, and neonatal intensive care admissions were more prevalent in women with HDP. Patterns of use of methyldopa were in-line with the Brazilian guidelines as the first-line therapy for HDP. However, the large number of women with HDP not using medications to manage HDP requires further investigation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".