The cumulative incidence of cisplatin‐induced hearing loss in young children is higher and develops at an early stage during therapy compared with older children based on 2052 audiological assessments
Bibliographic record
Abstract
BACKGROUND: Ototoxicity is a common adverse event of cisplatin treatment. The authors investigated the development of cisplatin-induced hearing loss (CIHL) over time in children with cancer by age and examined the influence of other clinical characteristics on the course of CIHL. METHODS: Data from Canadian patients with childhood cancer were retrospectively reviewed. Hearing loss was graded according to International Society of Pediatric Oncology criteria. The Kaplan-Meier method was applied to estimate the cumulative incidence of CIHL for the total cohort and according to age. Cox regression models were used to explore the effects of independent variables on CIHL development up to 3 years after the start of therapy. RESULTS: increase: hazard ratio [HR], 1.20; 95% CI, 1.01-1.41) vincristine (HR, 2.87; 95% CI, 1.89-4.36) and the total duration of concomitantly administered antibiotics (>30 days: HR, 1.85; 95% CI, 1.17-2.95) further influenced CIHL development over time. CONCLUSIONS: In young children, the cumulative incidence of CIHL is higher compared with that in older children and develops early during therapy. The course of CIHL is further influenced by the total cumulative dose of cisplatin and other ototoxic (co-)medication. These results highlight the need for audiological monitoring at each cisplatin cycle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".