Community level interventions for pre-eclampsia (CLIP) in India: A cluster randomised controlled trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it