Community level interventions for pre-eclampsia (CLIP) in India: A cluster randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: Pregnancy hypertension is associated with 7.1% of maternal deaths in India. The objective of this trial was to assess whether task-sharing care might reduce adverse pregnancy outcomes related to delays in triage, transport, and treatment. STUDY DESIGN: The Indian Community-Level Interventions for Pre-eclampsia (CLIP) open-label cluster randomised controlled trial (NCT01911494) recruited pregnant women in 12 clusters (initial four-cluster internal pilot) in Belagavi and Bagalkote, Karnataka. The CLIP intervention (6 clusters) consisted of community engagement, community health workers (CHW) provided mobile health (mHeath)-guided clinical assessment, initial treatment, and referral to facility either urgently (<4 h) or non-urgently (<24 h), dependent on algorithm-defined risk. Treatment effect was estimated by multi-level logistic regression modelling, adjusted for prognostically-significant baseline variables. Predefined secondary analyses included safety and evaluation of the intensity of mHealth-guided CHW-provided contacts. MAIN OUTCOME MEASURES: 20% reduction in composite of maternal, fetal, and newborn mortality and major morbidity. RESULTS: , and 401 facility referrals. Compared with intervention arm women without CLIP contacts, those with ≥8 contacts suffered fewer stillbirths (aOR 0.19 [0.10, 0.35]; p < 0.001), at the probable expense of survivable neonatal morbidity (aOR 1.39 [0.97, 1.99]; p = 0.072). CONCLUSIONS: As implemented, solely community-level interventions focussed on pre-eclampsia did not improve outcomes in northwest Karnataka.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".