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Record W2390468764

Effect of Occupational Manganese Exposure on Cognitive Function in Male Smelting Workers

2013· article· en· W2390468764 on OpenAlexaboutno aff
Jing Liu

Bibliographic record

VenueHuanjing yu zhiye yixue · 2013
Typearticle
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionEnvironmental healthSmeltingOccupational exposureMedicineBeijingCognitive Assessment SystemDemographyPsychologyCognitive impairmentMetallurgyGeographyChina
DOInot available

Abstract

fetched live from OpenAlex

[Objective]To explore the changes of cognitive function in male smelting workers with exposure to various levels of manganese(Mn) and its possible influencing factors.[Methods]A total of 240 male smelting workers were chosen from a Mn smelter in Guangxi and divided into high(86 workers) and low(154 workers) Mn exposure groups according to a cumulative exposure index(CEI);240 male workers of a sugar refinery in the same region were selected as the control group.All participants were required to complete questionnaires to collect general information and evaluate cognitive function by the Montreal Cognitive Assessment(MoCA) test(Beijing version).[Results]There was a negative association between cognitive function and the Mn cumulative exposure levels in male smelting workers(r=-0.324,P0.05).The MoCA scores of the 2 exposure groups were remarkably lower than that of the control group(P0.01),and the group with high Mn exposure level reported significantly lower scores than the group with low Mn exposure level(P0.05),after adjusted age,marital status,and other related variables.The results of multiple linear regression analysis showed that the worker’s cognitive function was significantly affected by age,education,Mn exposure level,and smoking(P0.05).[Conclusion]Occupational Mn exposure might decrease the cognitive function in male smelting workers.Moreover,the cognitive function could also be affected by age,education,and smoking.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

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

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 head0.046
GPT teacher head0.324
Teacher spread0.278 · 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 teacher head, 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

Citations0
Published2013
Admission routes1
Has abstractyes

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