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Record W2900360751 · doi:10.5271/sjweh.3781

Exposure to loud noise and risk of vestibular schwannoma: results from the INTERPHONE international case‒control study

2018· article· en· W2900360751 on OpenAlexaff
Isabelle Deltour, Brigitte Schlehofer, Amélie Massardier-Pilonchery, Klaus Schlaefer, Bruce K. Armstrong, Graham G. Giles, Jack Siemiatycki, Marie‐Élise Parent, Daniel Krewski, Mary L. McBride, Christoffer Johansen, Anssi Auvinen, Tiina Salminen, Martine Hours, L Montestrucq, Maria Blettner, Gabriele Berg‐Beckhoff, Siegal Sadetzki, Angela Chetrit, Susanna Lagorio, Ivano Iavarone, Naohito Yamaguchi, Toru Takebayashi, Alistair Woodward, Angus Cook, Tore Tynes, Lars Klæboe, Maria Feychting, Stefan Lönn, Sarah Fleming, Anthony J. Swerdlow, Minouk J. Schoemaker, Monika Moissonnier, Ausrele Kesminiene, Elisabeth Cardis, Joachim Schüz

Bibliographic record

VenueScandinavian Journal of Work Environment & Health · 2018
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsWilfrid Laurier UniversityUniversity of OttawaBC Cancer AgencyInstitut National de la Recherche ScientifiqueUniversité de Montréal
FundersAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailWorld Health Organization
KeywordsVestibular systemSchwannomaNoise exposureNoise (video)AudiologyMedicineHearing lossComputer scienceSurgeryArtificial intelligence

Abstract

fetched live from OpenAlex

Objective Studies of loud noise exposure and vestibular schwannomas (VS) have shown conflicting results. The population-based INTERPHONE case‒control study was conducted in 13 countries during 2000-2004. In this paper, we report the results of analyses on the association between VS and self-reported loud noise exposure. Methods Self-reported noise exposure was analyzed in 1024 VS cases and 1984 matched controls. Life-long noise exposure was estimated through detailed questions. Odds ratios (OR) and 95% confidence intervals (CI) were estimated using adjusted conditional logistic regression for matched sets. Results The OR for total work and leisure noise exposure was 1.6 (95% CI 1.4-1.9). OR were 1.5 (95% CI 1.3-1.9) for only occupational noise, 1.9 (95% CI 1.4-2.6) for only leisure noise and 1.7 (95% CI 1.2-2.2) for exposure in both contexts. OR increased slightly with increasing lag-time. For occupational exposures, duration, time since exposure start and a metric combining lifetime duration and weekly exposure showed significant trends of increasing risk with increasing exposure. OR did not differ markedly by source or other characteristics of noise. Conclusion The consistent associations seen are likely to reflect either recall bias or a causal association, or potentially indicate a mixture of both.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.511

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 studies0.0000.000
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 head0.013
GPT teacher head0.259
Teacher spread0.245 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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