A Strategic Program for Risk Assessment and Intervention to Mitigate Environmental Stressor-Related Adverse Pregnancy Outcomes in the Indian Population
Bibliographic record
Abstract
The Problem: Global environmental stressors of human health include, but are not limited to, conflict, migration, war, natural disasters, climate change, pollution, trauma, and pandemics. In combination with other factors, these stressors influence physical and mental as well as reproductive health. Maternal stress is a known factor for adverse pregnancy outcomes such as preterm birth (PTB); however, environmental stressors are less well-understood in this context and the problem is relatively under-researched. According to the WHO, major Indian cities including New Delhi are among the world's 20 most polluted cities. It is known that maternal exposure to environmental pollution increases the risk of premature births and other adverse pregnancy outcomes which is evident in this population. Response to the Problem: Considering the seriousness of this problem, an international and interdisciplinary group of researchers, physicians, and organizations dedicated to the welfare of women at risk of adverse pregnancy outcomes launched an international program named Optimal Pregnancy Environment Risk Assessment (OPERA). The program aims to discover and disseminate inexpensive, accessible tools to diagnose women at risk for PTB and other adverse pregnancy outcomes due to risky environmental factors as early as possible and to promote effective interventions to mitigate these risks. OPERA has been supported by the Worldwide Universities Network, World Health Organization (WHO) and March of Dimes USA. Addressing the Problem: This review article addresses the influence of environmental stressors on maternal-fetal health focusing on India as a model population and describes the role of OPERA in helping local practitioners by sharing with them the latest risk prediction and mitigation tools. The consequences of these environmental stressors can be partially mitigated by experience-based interventions that build resilience and break the cycle of inter- and-transgenerational transmission. The shared knowledge and experience from this collaboration are intended to guide and facilitate efforts at the local level in India and other LMIC to develop strategies appropriate for the jurisdiction for improving pregnancy outcomes in vulnerable populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".