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Record W2516922497

Pilot study on individual dose-response relationship evaluated through otoacoustic emission measurements in controlled noise exposure: influence of circadian rhythm

2016· article· en· W2516922497 on OpenAlexaffvenue
Vincent Nadon, Annelies Bockstael, Dick Botteldooren, Jérémie Voix

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAudiologyOtoacoustic emissionNoise (video)Circadian rhythmHearing lossNoise exposureMedicineAcoustic reflexRhythmAcousticsComputer science
DOInot available

Abstract

fetched live from OpenAlex

Over 22 million of North American workers are exposed daily to noise doses that may induce hearing loss. Unfortunately, current practices to prevent occupational noise-induced hearing loss ( NIHL ) based on group average of exposure/damage relationships do not account for the individual’s susceptibility. Consequently, NIHL remains one of the biggest cause of invalidity and indemnity in North America. To improve hearing conservation in the workplace, a procedure to continuously measure hearing fatigue using otoacoustic emissions ( OAE ) has been developed using a portable and robust OAE system designed for noisy field use. A pilot study has been conducted on human subjects in laboratory, playing back pre -recorded noise samples at realistic levels while recording the accumulated individual noise dose. To monitor the temporary effects (response) on the individuals’ inner-ear during the exposure, OAEs were measured periodically on subjects using either the designed OAE system or a reference OAE system. Audiometric thresholds, stapedius and medial olivocochlear reflex were also measured pre and post-exposure to monitor other potential effects on hearing. The potential effects of circadian rhythm on pre and post-exposure measurements are briefly studied here.

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)0.004
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.145
GPT teacher head0.388
Teacher spread0.243 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

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