Severe Hearing Loss in Children With Central Nervous System Tumors: A Population‐Based Cancer in Young People in Canada (CYP‐C) Report
Bibliographic record
Abstract
PURPOSE: Children with central nervous system (CNS) tumors are prone to treatment-related hearing loss (HL) and subsequent functional impairment. This study reports a dedicated population-based analysis of CNS tumor-specific rates and predictors of early severe HL. METHODS: A cohort study of children ≤15 years diagnosed with CNS tumors between 2001 and 2019 through the Cancer in Young People in Canada (CYP-C) program. The primary outcome was Grade 3 and 4 severe HL within 5 years following diagnosis. RESULTS: Among 3201 children with CNS tumors, 5.1% experienced early severe HL. Children with medulloblastoma (N = 570) and ATRT/other embryonal tumors (N = 269) had higher rates of early HL (16.1%, 15.2%, respectively). Cisplatin was administered to 80.1% of children with embryonal tumors, and 67.3% received radiotherapy. In children with medulloblastoma, age less than 6 years at diagnosis (OR 2.4, 1.5-3.8; vs. ≥6 years), radiation (OR 3.5, 1.6-7.6), and cisplatin (OR 20.4, 1.3-329.7) predicted early severe HL. Younger age at diagnosis doubled the probability of early severe HL (10.6% in <6 years vs. 4.8% in ≥6 years), while radiation exposure tripled the probability across age groups (29.8% and 10.6% if <6 years; 15.3% and 4.8% in ≥6 years). In children with ATRT/other embryonal tumors, cisplatin (OR 31.6, 1.9-521.9) was the sole predictor of early severe HL. CONCLUSIONS: High rates of early HL were observed in children with embryonal tumors. Younger children who received radiotherapy had higher probabilities of early HL, suggesting an additive interaction between age and radiation. Standardized otoprotection and research on cisplatin avoidance and therapy de-escalation in young children with embryonal tumors are urgently needed.
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 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 | 0.000 |
| 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".