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Record W3134043865

Clinical and Genome-wide Analysis of Cisplatin-induced Tinnitus Implicates Novel Ototoxic Mechanisms

2019· article· en· W3134043865 on OpenAlexaff
Omar El Charif, Brandon Mapes, Matthew R. Trendowski, Heather E. Wheeler, Claudia Wing, Paul C. Dinh, Robert D. Frisina, Darren R. Feldman, Robert J. Hamilton, David J. Vaughn, Chunkit Fung, Christian Kollmannsberger, Taisei Mushiroda, Michiaki Kubo, Eric R. Gamazon, Nancy J. Cox, Robert Huddart, Shirin Ardeshir‐Rouhani‐Fard, Patrick O. Monahan, Sophie D. Fosså, Lawrence H. Einhorn, Lois B. Travis, M. Eileen Dolan

Bibliographic record

VenuePMC · 2019
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversity of British ColumbiaPrincess Margaret Cancer Centre
Fundersnot available
KeywordsTinnitusCisplatinMedicineLogistic regressionOncologyOtotoxicityAudiologyInternal medicineChemotherapy
DOInot available

Abstract

fetched live from OpenAlex

Purpose:Cisplatin, a commonly used chemotherapeutic, results in tinnitus, the phantom perception of sound. Our purpose was to identify the clinical and genetic determinants of tinnitus among testicular cancer survivors (TCS) following cisplatin-based chemotherapy. Experimental Design: TCS (n= 762) were dichotomized to cases (moderate/severe tinnitus; n=154) and controls (none; n=608). Logistic regression was used to evaluate associations with comorbidities and SNP dosages in GWAS following quality control and imputation (covariates: age, noise exposure, cisplatin dose, genetic principal components). Pathway over-representation tests and functional studies in mouse auditory cells were performed. Results: Cisplatin-induced tinnitus (CisIT) significantly associated with age at diagnosis (P=0.007) and cumulative cisplatin dose (P=0.007). CisIT prevalence was not significantly greater in 400 mg/m2-treated TCS compared to 300 (P=0.41), but doses >400 mg/m2(median 580, range 402-828) increased risk by 2.61-fold (P

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 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 score0.541
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.066
GPT teacher head0.331
Teacher spread0.264 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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