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Record W1982095308 · doi:10.1177/0960327108093719

Occupational ototoxicity of <i>n</i> -hexane

2008· review· en· W1982095308 on OpenAlexafffundabout
Adolf Vyskočil, T. Leroux, Ginette Truchon, Martine Gendron, Naïma El Majidi, C. Viau

Bibliographic record

VenueHuman & Experimental Toxicology · 2008
Typereview
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de Montréal
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsOtotoxicityAudiologyHexaneHearing lossOccupational exposureToxicologyMedicineEnvironmental healthChemistryInternal medicineBiologyChromatography

Abstract

fetched live from OpenAlex

The ability of chemicals to produce hearing loss themselves or to promote noise-induced hearing loss has been reported for some organic solvents. The objective of this study was to review the literature on the effects of low-level exposure to n-hexane on the auditory system and consider its relevance for occupational settings. Both human and animal investigations were evaluated only for realistic exposure concentrations based on the permissible inhalation exposure limits. In Quebec, the time-weighted average exposure value (TWAEV) for 8 h is 50 ppm. In humans, the upper limit for considering ototoxicity data relevant to the occupational exposure situation was set at five times the TWAEV. Animal data were evaluated only for exposure concentrations up to 100 times the TWAEV. There is no convincing evidence of n-hexane-induced hearing loss in workers. In rats, n-hexane seems to affect auditory function; however, the site of these alterations cannot be determined from the present data. Further studies with sufficient data on the exposure of workers to n-hexane are necessary to make a definitive conclusion. In the interim, we recommend considering n-hexane as a possibly ototoxic agent.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.203
GPT teacher head0.428
Teacher spread0.225 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations10
Published2008
Admission routes3
Has abstractyes

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