Polybrominated Diphenyl Ether Concentrations in Human Breast Milk Specimens Worldwide
Bibliographic record
Abstract
BACKGROUND: Polybrominated diphenyl ethers (PBDEs) are a class of flame retardants of ubiquitous presence in numerous consumer products. PBDEs may impair neurodevelopment in infants. There is a lack of meta-analysis on PBDE concentrations in human breast milk worldwide. We aimed to summarize global research data on PBDE concentrations in human breast milk specimens in recent years. METHODS: We conducted a systematic review through PubMed search of original studies on PBDE concentrations in human individual breast milk specimens collected in the general population over the recent 15-year period (2000-2015) worldwide. RESULTS: A total of 49 eligible studies (total number of study subjects = 7,502) were identified. The pooled means (95% CI) of total PBDE concentration in breast milk (ng/g lipid) were 66.8 (44.7, 88.9) in North America, 2.6 (2.2, 3.1) in Europe, and 2.8 (2.4, 3.3) in Asia, respectively. The pooled means (95% CI) of median total PBDEs concentration in breast milk (ng/g lipid) were 40.0 (30.8-49.1) in North America, 1.9 (1.4-2.4) in Europe, and 2.2 (1.3-3.2) in Asia. The high concentrations of total PBDEs in breast milk in North America were mainly due to high concentrations of brominated diphenyl ether-47 (BDE-47), BDE-99, BDE-100, and BDE-153. There were too few studies from other continents (Africa, South America, and Oceania) for meaningful meta-analysis. CONCLUSION: Total PBDE concentrations in breast milk in the recent 15-year period were over 20 times higher in North America versus Asia or Europe, and comparable in Europe versus Asia. There is a need for more research data from other continents.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".