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Record W2004691691 · doi:10.3389/fneur.2015.00031

Salicylate-Induced Hearing Loss and Gap Detection Deficits in Rats

2015· article· en· W2004691691 on OpenAlexaff
Kelly E. Radziwon, Daniel Stolzberg, Maxwell E. Urban, Rachael Bowler, Richard Salvi

Bibliographic record

VenueFrontiers in Neurology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsWestern University
FundersNational Institute on Deafness and Other Communication DisordersOffice of Naval ResearchNational Institutes of Health
KeywordsTinnitusPsychoacousticsAudiologyStimulus (psychology)Hearing lossSodium salicylateNoise exposureNoise (video)PsychophysicsMedicinePsychologyNeuroscienceComputer sciencePerceptionInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

To test the "tinnitus gap-filling" hypothesis in an animal psychoacoustic paradigm, rats were tested using a go/no-go operant gap detection task in which silent intervals of various durations were embedded within a continuous noise. Gap detection thresholds were measured before and after treatment with a dose of sodium salicylate (200 mg/kg) that reliably induces tinnitus in rats. Noise-burst detection thresholds were also measured to document the amount of hearing loss and aid in interpreting the gap detection results. As in the previous human psychophysical experiments, salicylate had little or no effect on gap thresholds measured in broadband noise presented at high-stimulus levels (30-60 dB SPL); gap detection thresholds were always 10 ms or less. Salicylate also did not affect gap thresholds presented in narrowband noise at 60 dB SPL. Therefore, rats treated with a dose of salicylate that reliably induces tinnitus have no difficulty detecting silent gaps as long as the noise in which they are embedded is clearly audible.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.074
GPT teacher head0.285
Teacher spread0.211 · 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 designBench or experimental
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

Citations39
Published2015
Admission routes1
Has abstractyes

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