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Record W4308915290 · doi:10.1289/ehp10808

A Systematic Review of Mercury Exposures from Skin-Lightening Products

2022· review· en· W4308915290 on OpenAlexaff
Ashley Bastiansz, Jessica Ewald, Verónica Rodríguez Saldaña, Andrea Santa‐Rios, Niladri Basu

Bibliographic record

VenueEnvironmental Health Perspectives · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMercury (programming language)Interquartile rangeWeb of scienceMERCURY EXPOSUREEnvironmental healthScopusData extractionSystematic reviewMedicineMEDLINEToxicologyEnvironmental chemistryBiomonitoringComputer scienceChemistryBiologyMeta-analysisPathologySurgery

Abstract

fetched live from OpenAlex

Background: The Minamata Convention on Mercury (Article 4) prohibits the manufacture, import, or export of skin-lightening products containing mercury concentrations above 1 ppm. However, there is a lack of knowledge surrounding the global prevalence of mercury-added skin-lightening products. Objective: The objective of this study was to increase our understanding of worldwide human mercury exposure from skin-lightening products. Methods: A systematic search of peer-reviewed scientific literature was performed for relevant articles in four databases (PubMed, Web of Science Core Collection, Scopus, and TOXLINE). The search strategy, eligibility criteria, and data-extraction methods were established a priori. The search identified 2,303 unique scientific articles, of which 41 were ultimately deemed eligible for inclusion after iterative screens at the title, abstract, and whole-text levels. To facilitate data extraction and synthesis, all papers were organized according to four data groups a) “Mercury in products,” b) “Usage of products,” c) “Human biomarkers of exposure,” and d) “Health impacts.” Results: This review was based on data contained in 41 peer-reviewed scientific papers from 22 countries worldwide published between 2000 and 2022. In total, we captured mercury concentration values from 787 skin-lightening product samples [overall pooled central median mercury level was 0.49μg/g; interquartile range (IQR): 0.02–5.9] and 1,042 human biomarker measurements from 863 individuals. We also synthesized usage information from 3,898 individuals and self-reported health impacts associated with using mercury-added products from 832 individuals. Discussion: This review suggests that mercury widely exists as an active ingredient in many skin-lightening products worldwide and that users are at risk of variable and often high exposures. These synthesized findings identify data gaps and help increase our understanding of the health risks associated with the use of these products. https://doi.org/10.1289/EHP10808

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.037
GPT teacher head0.332
Teacher spread0.295 · 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 designSystematic review
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

Citations54
Published2022
Admission routes1
Has abstractyes

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