GLOBAL RESEARCH TRENDS AND GAPS ONMATERNALLEAD EXPOSURE AND CORTISOL: 25-YEARBIBLIOMETRICINSIGHT TOWARDS SUSTAINABLE DEVELOPMENTGOALS
Bibliographic record
Abstract
Lead (Pb) exposure during pregnancy poses significant risks to maternal and fetal health, notably throughitsassociation with elevated cortisol levels, a key stress biomarker. This bibliometric study analyzed global publicationtrends, research collaborations, and thematic focuses on Pb exposure and stress hormones in pregnant womenfrom1999 to 2024. The data were collected from the Scopus database through specific keywords and examined usingVOSviewer software to visualize co-authorship networks and track keyword development. Results reveal aconsistent growth in publications, with the United States contributing the largest share, followed by Canada, Brazil, and the United Kingdom. Research themes have evolved from general toxicity and oxidative stress toward specificoutcomes such as preeclampsia. DNA methylation and neurodevelopment. The results emphasize the global scientific consensus that Pb exposure represents a key environmental health concern, while also revealing notableresearch gaps in low- and middle-income nations. The implications of this study correspond to several SustainableDevelopment Goals (SDGs), most notably SDG 3 (Good Health and Well-being), SDG 5 (Gender Equality), SDG6(Clean Water and Sanitation), and SDG 11 (Sustainable Cities and Communities). Addressing these gaps will require interdisciplinary research, targeted policy interventions, and stronger international collaboration to protect vulnerable populations, especially pregnant women.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.050 | 0.116 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".