Development and Validation of a Novel Prediction Model for Hearing Loss From Cisplatin Chemotherapy
Bibliographic record
Abstract
PURPOSE: Cisplatin treats many common tumors but causes permanent and debilitating hearing loss (HL). The objective of this study was to develop and externally validate a predictive model of HL in cisplatin-treated children and adolescent cancer survivors. METHODS: The Pediatric Holistic Evaluation of Auditory Risk (PedsHEAR) model architecture used several machine learning approaches followed by an ensemble predictor. The primary end point was post-treatment communication-affecting HL (International Society of Pediatric Oncology Ototoxicity Scale [SIOP] Grade ≥2). PedsHEAR was developed from a multicenter data set of cisplatin-exposed patients up to 21 years old (1984-2017) and externally validated using data from the Children's Oncology Group ACCL05C1 study (2007-2012) and two combined institutional cohorts (1988-2022). The model predicts post-treatment HL in each patient (probability [%], 95% CI) and classifies patients as low, intermediate, or high risk for HL (probability HL <0.33, 0.33-0.60, >0.60, respectively). RESULTS: In the training data set (n = 1,115, median age 6.3 years, SIOP Grade ≥2 HL 44%), PedsHEAR demonstrated excellent discrimination (AUC, 0.93 [95% CI, 0.92 to 0.95]) and then successfully validated within the internal (testing; AUC, 0.79 [95% CI, 0.74 to 0.85]) and two external validation cohorts (AUC, 0.74 and AUC, 0.67). In an aggregate validation cohort (n = 631), the model predicted the probability of HL (AUC, 0.76 [95% CI, 0.72 to 0.79]) and classified 22% (141/631), 71% (447/631), and 7% (43/631) of patients as low, intermediate, or high risk for HL. CONCLUSION: PedsHEAR predicted SIOP Grade ≥2 HL in pediatric cisplatin-treated patients. This is the first validated model to successfully predict cisplatin-induced HL in a broadly representative population treated with diverse regimens across a range of treatment settings.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".