Where are the data to assess the safety of paternal drug exposure? A systematic review of secondary databases: A contribution from <scp>IMI</scp> concePTION
Bibliographic record
Abstract
BACKGROUND: Most studies assessing the safety of parental drug exposures during pregnancy and around the time of conception describe the effects of maternal exposure. Recent publications have raised awareness of the need for additional research regarding the safety of paternal drug exposures on pregnancy outcomes. OBJECTIVES: To identify and describe studies that use secondary databases in paternal drug safety studies and to describe the secondary databases that were used. METHODS: A systematic review of studies assessing paternal medication exposure and pregnancy and infant outcomes using secondary databases was performed. In addition, the secondary databases used for these studies was described. Literature search was conducted using Embase, Web of Science, and PubMed, over the period January 1, 2012 to April 30, 2023. For each eligible study, paternal drug exposure, outcome, and data source characteristics were extracted in a data extraction form. RESULTS: After reviewing the literature, 17 studies met inclusion criteria. The medications assessed for paternal safety were anti-rheumatic drugs (n = 10), anti-depressants (n = 3), anticonvulsants (n = 2), and anti-diabetes medications (n = 2). Pregnancy safety outcomes included congenital malformations, birth weight, and developmental disorders. The studies used five different databases across Europe and North America. The included studies used databases from Denmark (n = 12), Norway (n = 2), Sweden (n = 1), Canada (n = 1), and the United States (n = 1). The European studies utilized national patient registers that linked fathers to births and prescription histories. The North American databases used included insurance claims and electronic health records. CONCLUSIONS: Our review shows that few studies have been completed on paternal medication exposures and pregnancy outcomes, despite the availability of secondary databases that contain data necessary to link fathers to infants. More research on the potential adverse impacts of paternal medication exposures is needed.
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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 | 0.083 | 0.334 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".