Nutritional interventions to ameliorate the effect of endocrine disruptors on human reproductive health: A semi‐structured review from <scp>FIGO</scp>
Bibliographic record
Abstract
BACKGROUND: Endocrine disrupting chemicals have harmful effects on reproductive, perinatal, and obstetric outcomes. OBJECTIVE: To analyze the evidence on nutritional interventions to reduce the negative effects of endocrine disruptors on reproductive, perinatal, and obstetric outcomes. SEARCH STRATEGY: A search of MEDLINE (PubMed), Allied Health Literature (CINAHL), EMBASE, Web of Science, and the Cochrane Database was conducted from inception to May 2021. SELECTION CRITERIA: Experimental studies on human populations. DATA COLLECTION AND ANALYSIS: Data were collected from eligible studies. Risk of bias assessment was completed using the Cochrane risk of bias tool and the ROBINS-I Tool. RESULTS: Database searches yielded 15 362 articles. Removing 11 181 duplicates, 4181 articles underwent abstract screening, 26 articles were eligible for full manuscript review, and 16 met full inclusion criteria. Several interventions were found to be effective in reducing exposure to endocrine disruptors: avoidance of plastic containers, bottles, and packaging; avoidance of canned food/beverages; consumption of fresh and organic food; avoidance of fast/processed foods; and supplementation with vitamin C, iodine, and folic acid. There were some interventional studies examining therapies to improve clinical outcomes related to endocrine disruptors. CONCLUSION: Dietary alterations can reduce exposure to endocrine disruptors, with limited data on interventions to improve endocrine-disruptor-related clinical outcomes. This review provides useful instruction to women, their families, healthcare providers, and regulatory bodies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".