Recommendations for Age-Appropriate Testing, Timing, and Frequency of Audiologic Monitoring During Childhood Cancer Treatment
Bibliographic record
Abstract
IMPORTANCE: Ototoxicity is an irreversible direct and late effect of certain childhood cancer treatments. Audiologic surveillance during therapy as part of the supportive care pathway enables early detection of hearing loss, decision-making about ongoing cancer treatment, and, when applicable, the timely use of audiologic interventions. Pediatric oncologic clinical practice and treatment trials have tended to be driven by tumor type and tumor-specific working groups. Internationally accepted standardized recommendations for monitoring hearing during treatment have not previously been agreed on. OBJECTIVE: To provide standard recommendations on hearing loss monitoring during childhood cancer therapy for clinical practice. METHODS: An Ototoxicity Task Force was formed under the umbrella of the International Society of Paediatric Oncology, consisting of international audiologists, otolaryngologists, and leaders in the field of relevant pediatric oncology tumor groups. Consensus meetings conducted by experts were organized, aimed at providing standardized recommendations on age-directed testing, timing, and frequency of monitoring during cancer treatment based on literature and consensus. Consensus statements were prepared by the core group, adapted following several videoconferences, and finally agreed on by the expert panel. FINDINGS: The consensus reached was that children who receive ototoxic cancer treatment (platinum agents, cranial irradiation, and/or brain surgery) require a baseline case history, monitoring of their middle ear and inner ear function, and assessment of tinnitus at each audiologic follow-up. As a minimum, age-appropriate testing should be performed before and at the end of treatment. Ideally, audiometry with counseling before each cisplatin cycle should be considered in the context of the individual patient, specific disease, feasibility, and available resources. CONCLUSIONS AND RELEVANCE: This is an international multidisciplinary consensus report providing standardized supportive care recommendations on hearing monitoring in children undergoing potentially ototoxic cancer treatment. The recommendations are intended to improve the care of children with cancer and facilitate comparative research on the timing and development of hearing loss caused by different cancer treatment regimens.
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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 | 0.052 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".