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Record W4280563767 · doi:10.1177/2473974x221100545

Does N‐acetylcysteine Improve Established Hearing Loss in Guinea Pigs?

2022· article· en· W4280563767 on OpenAlexaffabout
Gia Gill, Brian W. Blakley

Bibliographic record

VenueOTO Open · 2022
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineOtotoxicityHearing lossAuditory brainstem responseNeomycinSalineRegimenCochleaGuinea pigSensorineural hearing lossAcetylcysteineAudiologyAnesthesiaAntioxidantSurgeryInternal medicineChemotherapyAntibioticsChemistry

Abstract

fetched live from OpenAlex

Objective To assess whether multiple injections of a powerful antioxidant can improve established sensorineural hearing loss in guinea pigs. Study Design Animal study. Setting Animal science laboratory, University of Manitoba. Methods A total of 16 guinea pigs were used in our study: 8 underwent unilateral intracochlear neomycin injection, and 8 underwent unilateral saline to serve as controls. After a period of 3 weeks for hearing loss to stabilize, 4 guinea pigs from each group received weekly intraperitoneal injections of N‐acetylcysteine (NAC) for 4 weeks. Click auditory brainstem response (ABR) testing was conducted at baseline, weekly after the start of NAC injections, and after the last injection. Pure tone ABR tests were conducted prior to intracochlear injections and at completion of the study. Results Click ABR thresholds were significantly worse in ears treated with neomycin ( P <. 001), as expected, but not significantly different when treated with NAC ( P =. 664). Thresholds for pure tone ABR were also not statistically different in neomycin‐treated ears with or without NAC ( P >. 99). Conclusions The aggressive antioxidant therapy performed in this study was not successful in improving established hearing loss via an antioxidant regimen that is known to change the oxidation‐reduction potential in the cochlea.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0020.004
Research integrity0.0000.001
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 head0.042
GPT teacher head0.305
Teacher spread0.263 · 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
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

Citations4
Published2022
Admission routes2
Has abstractyes

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