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Record W4323067018 · doi:10.1097/jom.0000000000002778

Association Between Serum Trace Heavy Metals and Liver Function Among Adolescents

2023· article· en· W4323067018 on OpenAlexaff
Daheng Yang, Haobo Zhu, Hongjian Chen, Guangfeng Long

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2023
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsOdds ratioAlanine aminotransferaseConfidence intervalLiver functionInternal medicineMedicineNational Health and Nutrition Examination SurveyLiver function testsGastroenterologyAlanine transaminaseZincHeavy metalsCholesterolPhysiologyEndocrinologyEnvironmental healthChemistryPopulationEnvironmental chemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Exposure to metals has been associated with liver-related disease. Few studies have explored the effect of sex stratification on adolescent liver function. METHOD: From the National Health and Nutrition Examination Survey (2011-2016), 1143 subjects aged 12-19 years were selected for analysis. The outcome variables were the levels of alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transpeptidase. RESULTS: The results showed a positive association between serum zinc and ALT in boys (odds ratio [OR], 2.37; 95% confidence interval [CI], 1.11-5.06). Serum mercury was associated with an increase in ALT level in girls (OR, 2.73; 95% CI, 1.14-6.57). Mechanistically, the efficacy mediated by total cholesterol accounted for 24.38% and 6.19% of the association between serum zinc and ALT. CONCLUSIONS: The results imply that serum heavy metals were associated with the risk of liver injury, possibly mediated by serum cholesterol, in adolescents.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.295
Teacher spread0.265 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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