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Record W4401396311 · doi:10.1080/1744666x.2024.2390024

Prenatal heavy metal exposure and pediatric asthma, allergic rhinitis, atopic dermatitis: a systematic review and meta-analysis

2024· review· en· W4401396311 on OpenAlexaboutno aff
Xi Chen, Shipeng Zhang, Dongxi Jiang, Yu‐Chuan Li, Man Yin, Caishan Fang, Zeyi Lv, Yue Huang, Hao Yang, Hui Zhang, Jianfeng Zhang, Qinwei Fu, Hanyu Wang, Wenjing Jiang, Yang Chen, Xinrong Li

Bibliographic record

VenueExpert Review of Clinical Immunology · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMedicineAtopic dermatitisAsthmaDermatologyPrenatal exposureMeta-analysisAllergyImmunologyPregnancyPathologyGestation

Abstract

fetched live from OpenAlex

OBJECTIVE: We review the prevalence of allergic diseases in children across prenatal exposures to heavy metals. METHODS: This systematic review and meta-analysis is registered in the PROSPERO database (CRD42023478471). A comprehensive search of PubMed, Web of Science, Medline and Cochrane library was conducted from the database inception until 31 October 2023. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of included studies. We used a random-effects model to summarize the effects from the studies. RESULTS: A total of 16 studies were included, 120,065 mother-child pairs enrolled. The NOS scores indicated that the quality of the literature included in the study was of a high standard. CONCLUSION: The final results indicate that prenatal exposure to Pb increased the incidence of wheeze and Eczema in infants, and exposure to Ni and CD increased the incidence of AD in infants.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.019
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.071
GPT teacher head0.396
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of Clinical ImmunologySame topicHeavy Metal Exposure and ToxicityFrench-language works237,207