Bibliographic record
Abstract
OBJECTIVE: Prenatal exposure to medications or environmental agents may contribute to adverse pregnancy outcomes and birth defects. This study provides a comprehensive bibliometric overview of the global research landscape on prenatal exposures and associated outcomes. METHODS: A systematic literature search was conducted in the Web of Science database on November 11, 2024. Articles and reviews addressing prenatal exposures and pregnancy outcomes, indexed in the Science Citation Index, Science Citation Index-Expanded, or Emerging Sources Citation Index, were screened. Data were analyzed and visualized using VOSviewer, the R-package Bibliometrix, and Microsoft Excel 2021. RESULTS: A total of 3,361 articles were analyzed. Publications on this topic have steadily increased over the past two decades, peaking in 2022. The United States emerged as the most productive country, followed by China and Canada. Gideon Koren was identified as the most prolific author, while Reproductive Toxicology published the highest number of articles. Among the keywords, "pregnancy" remained the most frequent overall; however, "placenta," "adverse pregnancy outcomes," and "systematic review" peaked in 2022, while "meta-analysis," "outcomes," and "stillbirth" peaked in 2021. CONCLUSION: This bibliometric study highlights the global evolution of scientific research on prenatal exposures and pregnancy outcomes. The findings offer valuable insights for clinicians, researchers, and policymakers, enabling a better understanding of the dynamic trends and emerging areas in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.173 | 0.226 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".