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Record W1977999879 · doi:10.1093/inthealth/ihu065

Betel-quid and alcohol use were associated with lipid accumulation product among male factory workers in Taiwan

2014· article· en· W1977999879 on OpenAlexaff
Chung‐Feng Huang, Chien‐Ting Chen, P.-M. Wang, Malcolm Koo

Bibliographic record

VenueInternational Health · 2014
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineBody mass indexWaistEnvironmental healthBetelAlcoholToxicologyInternal medicineDemographyBiologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: In this study, cardiometabolic risk associated with betel-quid, alcohol and cigarette use, based on a simple index-lipid accumulation product (LAP), was investigated in Taiwanese male factory workers. METHODS: Male factory workers were recruited during their annual routine health examination at a hospital in south Taiwan. The risk of cardiometabolic disorders was estimated by the use of LAP, calculated as (waist circumference [cm]-65)×(triglyceride concentration [mmol/l]). Multiple linear regression analyses were conducted to assess the risk factors of natural logarithm-transformed LAP. RESULTS: Of the 815 participants, 40% (325/815) were current alcohol users, 30% (248/815) were current smokers and 7% (53/815) were current betel-quid users. Current betel-quid use, alcohol use, older age, lack of exercise and higher body mass index were found to be significant and independent factors associated with natural logarithm-transformed LAP. CONCLUSIONS: Betel-quid and alcohol, but not cigarette use, were independent risk factors of logarithm-transformed LAP, adjusting for age, exercise and body mass index in male Taiwanese factory workers. LAP can be considered as a simple and useful method for screening of cardiometabolic risk.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.069
GPT teacher head0.369
Teacher spread0.300 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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