Long-Term Incidence and Predictors of Significant Hearing Loss Requiring Hearing Assistive Devices Among Childhood Cancer Survivors: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: Hearing loss is a significant late effect among childhood cancer survivors. Recent guidelines note insufficient evidence to quantify its natural history or risk associated with specific exposures. We examined the long-term incidence and predictors of hearing loss requiring hearing amplification devices (HADs) using population-based health care data. METHODS: In Ontario, Canada, HAD costs are subsidized by the Assistive Devices Program (ADP). Ontario children < 18 years of age at cancer diagnosis between 1987 and 2016 were identified and linked to ADP claims. Cumulative HAD incidence was compared between cases and matched controls. Patient, disease, and treatment predictors of HAD were examined. RESULTS: were not at greater risk, unlike those receiving higher cumulative doses. Relative to no cranial or facial radiation, those who had received ≤ 32.00 Gy were at no higher risk, unlike those who had received > 32.00 Gy. Carboplatin exposure was not associated with HAD. CONCLUSION: Childhood cancer survivors are at elevated risk for requiring HAD, which continues to increase between 20 and 30 years after diagnosis. Thresholds of cisplatin and radiation exposure exist, above which risk substantially increases. Prolonged monitoring and trials of otoprotective agents are warranted in high-risk populations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".