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Record W4417029509 · doi:10.1002/cpt.70160

Navigating the Genetic Risk of Chemotherapy‐Induced Hearing Loss in the Stria Vascularis

2025· article· en· W4417029509 on OpenAlexafffund
Tara Lazetic, Deanne Nixie R. Miao, Britt I. Drögemöller, Alain Dabdoub, Julia M. Abitbol

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsSunnybrook Health Science CentreResearch Institute in Oncology and HematologyChildren's Hospital of WinnipegSunnybrook HospitalCancerCare ManitobaChildren's Hospital Research Institute of ManitobaUniversity of ManitobaUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsOtotoxicityHearing lossTranscriptomeGenetic testingIncidence (geometry)Inner earRisk assessment

Abstract

fetched live from OpenAlex

Cisplatin is a chemotherapy drug that causes permanent hearing loss by damaging a critical tissue lining the inner ear, called the stria vascularis (SV). Currently, the molecular mechanisms of SV damage are largely unknown and the incidence of ototoxicity in patients cannot be reliably predicted. Growing evidence suggests certain genetic variants expressed in the SV are significant risk factors for ototoxicity, which may be leveraged to better understand cisplatin-induced hearing loss. Also highlighted are innovative developments in integrating genomic and transcriptomic data through multi-omic approaches that may be translated to improve future genetic testing and otoprotectant development.

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)0.001
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: Theoretical or conceptual · 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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.128
GPT teacher head0.457
Teacher spread0.329 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes2
Has abstractyes

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