Unlocking insights from mother-infant linked data in pharmacoepidemiology: opportunities, challenges, and future directions
Bibliographic record
Abstract
Pregnant women and newborns are historically underrepresented in clinical trials, creating critical gaps in evidence on the safety and effectiveness of medications used during pregnancy. Real-world data (RWD) sources offer a promising avenue to address these gaps. To fully realize this potential, it is essential to link maternal and infant records accurately within and across diverse datasets. High-quality mother--infant linkage enables the robust evaluation of maternal medication use and its short- and long-term effects on both maternal and infant health. However, linking maternal and infant healthcare data introduces complex methodological and practical challenges. Achieving accurate linkage is often hindered by factors such as inconsistent personal identifiers, discrepancies in insurance coverage between mother and infant, data incompleteness, algorithmic accuracy, and strict data privacy regulations. Commonly used proxies for linkage (e.g. shared address or healthcare provider) may also be unreliable and can introduce misclassification or duplication. This commentary synthesizes current knowledge on mother--infant data linkage in RWD. It also systematically outlines the key challenges, emerging opportunities, and strategic directions to improve linkage quality and address privacy concerns in the identification of mother--infant dyads to support rigorous pharmacoepidemiologic research on maternal and infant health outcomes. By improving linkage methods and leveraging innovative approaches such as tokenization and validated algorithms, researchers can enhance the reliability of real-world evidence on maternal and infant health outcomes, including long-term follow-up across diverse data sources. Advancing these methodological frontiers is essential to generate evidence that supports safer and more informed treatment decisions for pregnant women and their children.
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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 | 0.169 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.031 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".