The project to understand and research preterm pregnancy outcomes and stillbirths in South Asia (PURPOSe): a protocol of a prospective, cohort study of causes of mortality among preterm births and stillbirths
Bibliographic record
Abstract
BACKGROUND: In South Asia, where most stillbirths and neonatal deaths occur, much remains unknown about the causes of these deaths. About one-third of neonatal deaths are attributed to prematurity, yet the specific conditions which cause these deaths are often unclear as is the etiology of stillbirths. In low-resource settings, most women are not routinely tested for infections and autopsy is rare. METHODS: This prospective, cohort study will be conducted in hospitals in Davengere, India and Karachi, Pakistan. All women who deliver either a stillbirth or a preterm birth at one of the hospitals will be eligible for enrollment. With consent, the participant and, when applicable, her offspring, will be followed to 28-days post-delivery. A series of research tests will be conducted to determine infection and presence of other conditions which may contribute to the death. In addition, all routine clinical investigations will be documented. For both stillbirths and preterm neonates who die ≤ 28 days, with consent, a standard autopsy as well as minimally invasive tissue sampling will be conducted. Finally, an expert panel will review all available data for stillbirths and neonatal deaths to determine the primary and contributing causes of death using pre-specified guidance. CONCLUSION: This will be among the first studies to prospectively obtain detailed information on causes of stillbirth and preterm neonatal death in low-resource settings in Asia. Determining the primary causes of death will be important to inform strategies most likely to reduce the high mortality rates in South Asia. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT03438110 ) Clinical Trial Registry of India ( CTRI/2018/03/012281 ).
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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 | 0.077 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".