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Record W2769136898 · doi:10.1097/jom.0000000000001232

Occupational Noise Exposure, Bilateral High-Frequency Hearing Loss, and Blood Pressure

2017· article· en· W2769136898 on OpenAlexaff
Wenqi Gan, David M. Mannino

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsBC Centre for Disease Control
Fundersnot available
KeywordsMedicineHearing lossBlood pressureAudiologyAbsolute threshold of hearingNoise-induced hearing lossNoise exposureNational Health and Nutrition Examination SurveyIndustrial noiseOccupational exposureHeart rateAudiometryCardiologyInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to investigate the relationships between occupational noise exposure and blood pressure using self-reported occupational exposure and bilateral high-frequency hearing loss. METHODS: This study included 4548 participants aged 20 to 69 years from the National Health and Nutrition Examination Survey 1999 to 2004. On the basis of self-reported exposure status, participants were divided into the current, former, or never exposed groups. Bilateral high-frequency hearing loss was defined as the average high-frequency hearing threshold at least 25 dB in both ears. RESULTS: The currently exposed participants had slightly increased diastolic blood pressure compared with those never exposed. Among previously exposed participants, those with bilateral high-frequency hearing loss had increased systolic blood pressure, heart rate, and the prevalence of hypertension compared with those with normal high-frequency hearing. CONCLUSION: Although there were some significant results, the evidence was not consistent to support the associations between occupational noise exposure and blood pressure.

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)0.001
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 score0.005
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.041
GPT teacher head0.357
Teacher spread0.316 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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