Regulations Governing Medicines for Maternal and Neonatal Health: A Landscape Assessment
Bibliographic record
Abstract
Limited evidence related to the safety or efficacy of medicines in pregnancy and during breastfeeding is available to inform patients and healthcare professionals. Understanding the current regulatory landscape in the clinical trial and postmarketing settings is critical to facilitate the development of applicable processes and tools for studying medicine use during pregnancy and breastfeeding and comply with health authority expectations. This review summarizes key findings from a landscape assessment of regulations, guidelines, and guidance on the use of medicines in pregnancy and breastfeeding issued by health authorities in various territories (including the Americas, Europe, Africa, and Asia Pacific) and outlines relevant initiatives undertaken by health authorities, academic institutions, industry consortia, and public-private organizations. While global pharmacovigilance legislation regarding medication use during pregnancy and breastfeeding exists and continues to evolve, the landscape assessment revealed that there is a lack of global legislative harmonization in both the clinical trial and postmarketing surveillance settings and regulatory gaps still exist in many countries/regions. Despite ongoing efforts from health authorities and public and private organizations, intensive efforts for legislation harmonization and stakeholder collaboration are required to improve the current environment of medication safety in pregnancy and breastfeeding.
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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 | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".