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Record W2085831649 · doi:10.1121/1.4787678

The combined effect of noise and carbon monoxide on hearing thresholds of exposed workers

2005· article· en· W2085831649 on OpenAlexaffabout
Adriana Bender Moreira de Lacerda, Tony Leroux, Jean-Pierre Gagn

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAudiogramCarbon monoxideNoise (video)AudiologyNoise-induced hearing lossHearing lossAbsolute threshold of hearingMedicineNoise exposureChemistryComputer scienceBiochemistry

Abstract

fetched live from OpenAlex

Animal models have been used to demonstrate the potentiation of noise-induced hearing loss (NIHL) by carbon monoxide. It has been shown that the addition of carbon monoxide to otherwise safe noise exposure levels produces significant NIHL in rats. However, the effects of chronic exposure to low level of carbon monoxide in a noisy work environment are still unknown. The aim of this study was to compare the hearing thresholds of a group of workers exposed to noise and carbon monoxide (Group 1) to another group of workers where carbon monoxide exposure is absent or negligible (Group 2). The analysis was based on 9396 audiograms collected by the Quebec National Public Health Institute between 1983 and 1996. The results show significantly poorer hearing thresholds at high frequencies (3, 4, and 6 kHz) for the carbon monoxide exposed group (p<0.001). The potentiation effect also varied according to years of exposure in work place; a larger effect is observed for workers with between 15 to 20 years of exposure (p<0.001). This study provides the first demonstration of a potentiation effect of NIHL by carbon monoxide in humans.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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 designBench or experimental
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

Citations17
Published2005
Admission routes2
Has abstractyes

Explore more

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