MétaCan
Menu
Back to cohort

Hearing loss after cisplatin-based chemotherapy: Patient-reported outcomes versus audiometric assessments.

2021· article· en· W3166408331 on OpenAlexaff
Shirin Ardeshir‐Rouhani‐Fard, Sophie D. Fosså, Robert Huddart, Patrick O. Monahan, Chunkit Fung, Yiqing Song, M. Eileen Dolan, Darren R. Feldman, Robert J. Hamilton, David J. Vaughn, Neil E. Martin, Christian Kollmannsberger, Paul C. Dinh, Lifang Hou, Yinan Zheng, Lawrence H. Einhorn, Robert D. Frisina, Lois B. Travis

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicEar and Head Tumors
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health Network
FundersNational Institutes of Health
KeywordsMedicineLogistic regressionConcordanceTinnitusMultinomial logistic regressionHearing lossAudiogramAudiologyInternal medicine

Abstract

fetched live from OpenAlex

5016 Background: Although pure-tone audiometry is the gold standard to evaluate hearing loss (HL), patient-reported outcomes are practically more time and cost effective. However, no data exist on factors associated with discrepancies between patient-reported and audiometrically-defined HL in adult-onset cancer survivors after cisplatin-based chemotherapy (CBCT); and few comprehensive assessments of factors associated with audiometrically-defined HL have been conducted. Methods: A total of 1,410 testicular cancer survivors (TCS) ≥6 months post-CBCT completed comprehensive audiometric assessments (0.25-12 kHz) and detailed questionnaires of sociodemographic, clinical, and health behaviors. Audiometrically-defined HL severity was defined using American Speech-Language-Hearing Association (ASHA) criteria. Multivariable multinomial logistic regression identified factors associated with discrepancies (overestimation and underestimation vs. concordance), between patient-reported and audiometrically-defined HL and multivariable ordinal logistic regression evaluated factors associated with the HL severity. Results: Overall, 34.8% of TCS self-reported HL, while 77.8% had audiometrically-defined HL. Among TCS without tinnitus, those with audiometrically-defined HL at only extended high frequencies (EHFs) (10-12 kHz) (17.8%) or at both EHFs and standard frequencies (0.25-8 kHz) (23.4%) were significantly more likely to self-report HL than those with no audiometrically-defined HL (8.1%) (OR = 2.48; 95%CI, 1.31-4.68 and OR = 3.49; 95%CL,1.89-6.44, respectively). Older age (OR = 1.09; P< 0.0001), absence of prior noise exposure (OR = 1.40; P= 0.02), and mixed/conductive HL (OR = 2.01; P= 0.0007) were associated with greater underestimation of audiometrically-defined HL severity. Hearing aid use (OR = 0.18; P= 0.003) and higher education ( P= 0.004) were associated with less underestimation of audiometrically-defined HL severity, while tinnitus was associated with greater overestimation ( P< 0.0001). Older age (OR = 1.13; P< 0.0001), cumulative cisplatin dose ( > 300 mg/m 2, OR = 1.47; P= 0.0001), and hypertension (OR = 1.80; P= 0.0007) were associated with greater ASHA-defined HL severity, whereas post-graduate education (OR = 0.58; P= 0.005) was associated with less severe HL. Conclusions: Discrepancies between patient-reported and audiometrically-defined HL after CBCT are associated with several factors including age, education, tinnitus, prior noise exposure, use of hearing aids, and conductive HL. Understanding these factors will help clinicians to better interpret self-reported HL as a surrogate for audiometric assessments. For survivors who self-report HL, but have normal audiometric findings at standard frequencies, referral to an audiologist for additional testing and inclusion of EHFs in audiometric assessments, should be considered.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0010.000

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 head0.182
GPT teacher head0.515
Teacher spread0.333 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicEar and Head TumorsFrench-language works237,207