Impact of phthalate exposure on gestational diabetes mellitus: a systematic review
Bibliographic record
Abstract
Background: Gestational Diabetes Mellitus (GDM) is a transient diabetogenic state that often leads to adverse maternal and fetal outcomes. The rising burden of exposure to endocrine-disrupting chemicals like phthalates essentially disrupts the tightly regulated endocrine system, thereby modulating the insulin signaling pathways, leading to GDM. Objective: In the present work, a systematic review was performed to examine the probable relation between maternal exposure to phthalates, as endocrine-disrupting compounds, and GDM. Methods: Relevant studies from their inception to April 2025 were identified by searching PubMed, Embase, Scopus, and Science Direct. The data were screened using the Rayyan tool, and the risk of bias was assessed using the New Castle Ottawa Scale selection tool. Results: We identified 13 studies that showed a significant presence of phthalates in the urine samples of GDM patients. 5 phthalate secondary metabolites, Monoethyl Phthalate, Monobutyl phthalate, Mono-Isobutyl Phthalate, and Monobenzyl Phthalate and the primary phthalate Di(2-ethylhexyl) Phthalate were found to be most commonly present in the urine samples of the GDM patients. Conclusion: Urinary phthalate levels can be used as a non-invasive biomarker for GDM, thereby also reducing the risk of associated adverse pregnancy outcomes. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251023656.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".