Choosing blood pressure thresholds to inform pregnancy care in the community: An analysis of cluster trials
Bibliographic record
Abstract
Abstract Objective To inform digital health design by evaluating diagnostic test properties of antenatal blood pressure (BP) outputs and levels to identify women at risk of adverse outcomes. Design Planned secondary analysis of cluster randomised trials. Setting India, Pakistan, Mozambique. Population Women with in‐community BP measurements and known pregnancy outcomes. Methods Blood pressure was defined by its outputs (systolic and/or diastolic, systolic only, diastolic only or mean arterial pressure [calculated]) and level: normotension‐1 (<135/85 mmHg), normotension‐2 (135–139/85–89 mmHg), non‐severe hypertension (140–149/90–99 mmHg; 150–154/100–104 mmHg; 155–159/105–109 mmHg) and severe hypertension (≥160/110 mmHg). Dose–response (adjusted risk ratio [aRR]) and diagnostic test properties (negative [−LR] and positive [+LR] likelihood ratios) were estimated. Main Outcome Measures Maternal/perinatal composites of mortality/morbidity. Results Among 21 069 pregnancies, different BP outputs had similar aRR, −LR, and +LR for adverse outcomes. No BP level (even normotension‐1) was associated with low risk (all −LR ≥0.20). Across outcomes, risks rose progressively with higher BP levels above normotension‐1. For each of maternal central nervous system events and stillbirth, BP ≥155/105 mmHg showed at least good diagnostic test performance (+LR ≥5.0) and BP ≥135/85 mmHg at least fair performance, similar to BP ≥140/90 mmHg (+LR 2.0–4.99). Conclusions In the community, normal BP values do not provide reassurance about subsequent adverse outcomes. Given the similar performance of BP cut‐offs of 135/85 and 140/90 mmHg for hypertension, and 155/105 and 160/110 mmHg for severe hypertension, digital decision support for women in the community should consider using these lower thresholds.
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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 | 0.141 | 0.226 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".