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Record W2102442469 · doi:10.7727/wimj.2013.034

'Fishy' Make-up on the Hook for Mercury Exposure: A Case Series

2014· article· en· W2102442469 on OpenAlexafffundabout
Olivia Drescher, E. Dewailly, Mike Krimholtz, Jonathan Rutchik

Bibliographic record

VenueWest Indian Medical Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsCentre de Développement du Porc du Québec
FundersInternational Development Research CentreInstitut National de Santé Publique du Québec
KeywordsMercury (programming language)MedicineUrineMERCURY EXPOSURECosmeticsMercury poisoningToxicologyPhysiologyEnvironmental healthEnvironmental chemistryPathologyInternal medicineBiomonitoringToxicityBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: This report examines the source and outcome of four Barbadian women with extremely high hair mercury concentrations (361-5617 µg/g inorganic mercury) due to topical application of mercury containing skin-lightening cosmetics. METHODS: Inorganic hair and urine mercury analysis was done at the toxicological centre laboratory of the Institut National de Santé Publique du Québec (Standard Council of Canada accredited). The clinical examinations were performed on location at the Queen Elizabeth Hospital of Barbados. RESULTS: Urine samples [7-135 µg/L, normal < 2 µg/L] revealed elevated mercury concentrations signifying systemic exposure. Reported symptoms during the clinical examination were consistent but nonspecific to chronic mercury exposure. CONCLUSION: Evidently, cosmetics containing dangerous levels of mercury are still available for purchase in Barbados and should be entirely banned.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.263
Teacher spread0.246 · 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 designCase report
Domainnot available
GenreEmpirical

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
Published2014
Admission routes3
Has abstractyes

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