How to manage hypertension in pregnancy effectively
Bibliographic record
Abstract
The hypertensive disorders of pregnancy (HDP) are a leading cause of maternal mortality and morbidity in both well and under-resourced settings. Maternal, fetal, and neonatal complications of the HDP are concentrated among, but not limited to, women with pre-eclampsia. Pre-eclampsia is a systemic disorder of endothelial cell dysfunction and as such, blood pressure (BP) treatment is but one aspect of its management. The most appropriate BP threshold and goal of antihypertensive treatment are controversial. Variation between international guidelines has more to do with differences in opinion rather than differences in published data. For women with severe hypertension [defined as a sustained systolic BP (sBP) of ≥160 mmHg and/or a diastolic BP (dBP) of ≥110 mmHg], there is consensus that antihypertensive therapy should be given to lower the maternal risk of central nervous system complications. The bulk of the evidence relates to parenteral hydralazine and labetalol, or to oral calcium channel blockers such as nifedipine capsules. There is, however, no consensus regarding management of non-severe hypertension (defined as a sBP of 140-159 mmHg or a dBP of 90-109 mmHg), because the relevant randomized trials have been underpowered to define the maternal and perinatal benefits and risks. Although antihypertensive therapy may decrease the occurrence of BP values of 160-170/100-110 mmHg, therapy may also impair fetal growth. The potential benefits and risks do not seem to be associated with any particular drug or drug class. Oral labetalol and methyldopa are used most commonly, but many different β-adrenoceptor blockers and calcium channel blockers have been studied in clinical trials.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".