Do Commonly Used Oral Antihypertensives Alter Fetal or Neonatal Heart Rate Characteristics? A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To examine fetal (FHR) and neonatal heart rate patterns following use of common oral antihypertensives in pregnancy. METHODS: A systematic review of randomized controlled trials (RCTs), observational studies (N >/= 6 women), and animal studies. Data were abstracted (two reviewers) to determine relative risk (RR) (or risk difference (RD) for low event rates) and 95% CI. RESULTS: Eighteen RCTs (1858 women), one controlled observational study (N = 22), and seven case series (N = 117) were reviewed. Most hypertension was pregnancy-induced (N = 14 studies). The FHR was assessed by cardiotocogram (CTG) (N = 17 studies (visual interpretation); 1 study (computerized CTG), or umbilical artery velocimetry (N = 4). Four studies examined neonatal heart rate. In placebo-controlled RCTs (N = 192 women), adverse FHR effects did not differ between groups [9/101 (drugs) vs. 7/91 (placebo); RD 0.02, 95% CI (- 0.06, 0.11); chi2 = 1.02]. In six drug vs. drug RCTs (295 women), adverse FHR effects did not differ between groups [29/144 (methyldopa) vs. 42/151 (other drugs); RR 0.72, 95% CI (0.49, 1.07); chi2 = 0.69]. In one labetalol vs. placebo trial, neonatal bradycardia did not differ between groups [4/70 (labetalol) vs. 4/74 (placebo); OR 1.06, 95% CI (0.26, 4.39)], while in three drug vs. drug RCTs, neonatal bradycardia was not observed (0/24 vs. 0/26). CONCLUSIONS: Available data are inadequate to conclude whether oral methyldopa, labetalol, nifedipine, or hydralazine adversely affect fetal or neonatal heart rate and pattern. Until definitive data are available, FHR changes cannot be reliably attributed to drug effect, but may be due to progression of the underlying maternal or placental disease.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".