The role of genetic variants in the prediction of hearing loss due to cisplatin chemoradiotherapy
Bibliographic record
Abstract
BACKGROUND: Concomitant high-dose cisplatin with radiotherapy is commonly used for treating head and neck squamous cell carcinoma (HNSCC). Cisplatin, often used with radiotherapy, is known for causing irreversible sensorineural hearing loss, with individual variability suggesting a genetic component. This study aims to enhance the predictive ability of the clinical prediction model for cisplatin-induced hearing loss (CIHL) in HNSCC patients, as outlined in Theunissen et al., by incorporating significant genetic variants. METHODS: Conducted at the Netherlands Cancer Institute, this retrospective study included 74 patients treated between 1997 and 2011. Thirty-one SNPs that were previously associated with CIHL or other cisplatin-induced toxicities were identified and incorporated into the model. The primary outcome measured was the change in decibels at posttreatment 1-2-4 kHz hearing levels per additional minor allele of these SNPs, evaluated using linear mixed-effects regression models. The model's predictive accuracy was determined by the area under the curve (AUC) using 10-fold cross-validation. RESULTS: The rs2289669 SNP in the SLC47A1/MATE1 gene was linked to a significant 2.67 dB increase in hearing loss per allele (95% CI 0.49-4.86, p = 0.017). Incorporating rs2289669 improved the model's AUC from 0.78 to 0.83, a borderline significant improvement (p = 0.073). CONCLUSIONS: This study underscores the importance of the rs2289669 SNP in CIHL and demonstrates the potential of combining genetic and clinical data for enhanced predictive models in personalized treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".