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Record W2417471564

Hearing thresholds under acute hypoxia and relationship to slowing in the auditory modality.

2000· article· en· W2417471564 on OpenAlexaff
B Fowler, Arthur C. Grant

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsYork University
Fundersnot available
KeywordsAudiologyHypoxia (environmental)LoudnessBreathingHypoxic hypoxiaPsychologyMedicineAnesthesiaOxygenChemistry
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Acute hypoxia slows reaction time to visual stimuli and it has been proposed that this slowing can be accounted for by a decrease in perceived brightness. Auditory slowing could be accounted for in an analogous manner if hypoxia decreases perceived loudness, as reflected by an increase in pure-tone thresholds. However, evidence concerning the effects of hypoxia on auditory thresholds is contradictory and we performed an experiment to clarify this issue. Pilot work suggested that thresholds were raised due to noise from our breathing apparatus used to deliver the low O2 mixtures and we eliminated this artifact by measuring thresholds while the subjects held their breath. METHODS: The six subjects breathed either air as a control through an oro-nasal mask or low O2 mixtures to maintain arterial blood oxygen saturation at 74% while thresholds between 500-4000 Hz were measured with an audiometer during breathholding. RESULTS: Hypoxia produced a statistically significant but practically insignificant decrease in thresholds of 1 dB across all frequencies tested. CONCLUSIONS: Our results are consistent with the view that audition is relatively insensitive to hypoxia and that the slowing observed with auditory stimuli cannot be accounted for by an increase in auditory thresholds. Some alternative hypotheses which could account for this slowing are proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.281
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2000
Admission routes1
Has abstractyes

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