Comprehensive association analysis of speech recognition thresholds after cisplatin‐based chemotherapy in survivors of adult‐onset cancer
Bibliographic record
Abstract
Abstract Purpose Deficits in speech understanding constitute one of the most severe consequences of hearing loss. Here we investigate the clinical and genetic risk factors for symmetric deterioration of speech recognition thresholds (SRT) among cancer survivors treated with cisplatin. Methods SRT was measured using spondaic words and calculating the mean of measurements for both ears with symmetric SRT values. For clinical associations, SRT‐based hearing disability (SHD) was defined as SRT≥15 dB hearing loss and clinical variables were derived from the study dataset. Genotyped blood samples were used for GWAS with rank‐based inverse normal transformed SRT values as the response variable. Age was used as a covariate in association analyses. Results SHD was inversely associated with self‐reported health (p = 0.004). Current smoking (p = 0.002), years of smoking (p = 0.02), BMI (p < 0.001), and peripheral motor neuropathy (p = 0.003) were positively associated with SHD, while physical activity was inversely associated with SHD (p = 0.005). In contrast, cumulative cisplatin dose, peripheral sensory neuropathy, hypertension, and hypercholesterolemia were not associated with SHD. Although no genetic variants had an association p value < 5 × 10−8, 22 genetic variants were suggestively associated (p < 10−5) with SRT deterioration. Three of the top variants in 10 respective linkage disequilibrium regions were either positioned within the coding sequence or were eQTLs for genes involved in neuronal development (ATE1, ENAH, and ZFHX3). Conclusion Current results improve our understanding of risk factors for SRT deterioration in cancer survivors. Higher BMI, lower physical activity, and smoking are associated with SHD. Larger samples would allow for expansion of the current findings on the genetic architecture of SRT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".