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Record W1974426938 · doi:10.1136/oem.2008.042101

Can loud noise cause acoustic neuroma? Analysis of the INTERPHONE study in France

2009· article· en· W1974426938 on OpenAlexaff
Martine Hours, M. Bernard, M Arslan, L Montestrucq, L. Richardson, Isabelle Deltour, E Cardis

Bibliographic record

VenueOccupational and Environmental Medicine · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsAcoustic neuromaNoise (video)Noise exposureAudiologyAcousticsMedicineAcoustic traumaComputer scienceCochleaArtificial intelligencePhysicsHearing loss

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate possible associations between risk of acoustic neuroma and exposure to loud noise in leisure and occupational settings. METHODS: A case-control study was conducted in France within the international INTERPHONE study. The cases were the 108 subjects diagnosed with acoustic neuroma between 1 June 2000 and 31 August 2003. Two controls per case were selected from the electoral rolls and individually matched for gender, age (5 years) and area (local authority district) of residence at the time of the case diagnosis. Multivariate analyses were conducted using conditional logistic regression. Adjustment was made for socioeconomic status. RESULTS: Acoustic neuroma was found to be associated with loud noise exposure (odds ratio (OR) = 2.55; 95% CI 1.35 to 4.82), both in leisure settings, particularly when listening to loud music (OR = 3.88; 95% CI 1.48 to 10.17) and at work (OR = 2.26; 95% CI 1.08 to 4.72). This risk increased with exposure duration (>6 years' leisure exposure: OR = 3.15; 95% CI 1.07 to 9.24). Risk varied according to the type of noise (continuous or explosive vs intermittent). CONCLUSION: The present results agree with other recent reports implicating loud noise in the risk of acoustic neuroma.

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 score0.280
Threshold uncertainty score0.327

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 head0.023
GPT teacher head0.274
Teacher spread0.250 · 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

Citations37
Published2009
Admission routes1
Has abstractyes

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