Population-level data on antenatal screening for proteinuria; India, Mozambique, Nigeria, Pakistan
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence and prognosis of proteinuria at enrolment in the 27 intervention clusters of the Community-Level Interventions for Pre-eclampsia cluster randomized trials. METHODS: We identified pregnant women eligible for inclusion in the trials in their communities in four countries (2013-2017). We included women who delivered by trial end and received an intervention antenatal care visit. The intervention was a community health worker providing supplementary hypertension-oriented care, including proteinuria assessment by visual assessment of urinary dipstick at the first visit and all subsequent visits when hypertension was detected. In a multilevel regression model, we compared baseline prevalence of proteinuria (≥ 1+ or ≥ 2+) across countries. We compared the incidence of subsequent complications by baseline proteinuria. FINDINGS: Baseline proteinuria was detected in less than 5% of eligible pregnancies in each country (India: 234/6120; Mozambique: 94/4234; Nigeria: 286/7004; Pakistan: 315/10 885), almost always with normotension (India: 225/234; Mozambique: 93/94; Nigeria: 241/286; Pakistan: 264/315). There was no consistent relationship between baseline proteinuria (either ≥ 1+ or ≥ 2+) and progression to hypertension, maternal mortality or morbidity, birth at < 37 weeks, caesarean section delivery or perinatal mortality or morbidity. If proteinuria testing were restricted to women with hypertension, we projected annual cost savings of 153 223 981 United States dollars (US$) in India, US$ 9 055 286 in Mozambique, US$ 53 181 933 in Nigeria and US$ 38 828 746 in Pakistan. CONCLUSION: Our findings question the recommendations to routinely evaluate proteinuria at first assessment in pregnancy. Restricting proteinuria testing to pregnant women with hypertension has the potential to save resources.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".