Poster ‐ 31: Predicting IQ and hearing loss following radiotherapy in pediatric brain tumors: proton vs photon
Bibliographic record
Abstract
Purpose: The increased sparing of normal tissues in intensity modulated proton therapy (IMPT) in pediatric brain tumor treatments should translate into improved neurocognitive outcomes. Models were used to estimate the intelligence quotient (IQ) and the risk of hearing loss 5 years post radiotherapy and to compare outcomes of proton against photon in pediatric brain tumors. Methods: Patients who had received intensity modulated radiotherapy (IMRT) were randomly selected from our retrospective database. The existing planning CT and contours were used to generate IMPT plans. The RBE‐corrected dose was calculated for both IMPT and IMRT. For each patient, the IQ was estimated via a Monte Carlo technique, whereas the reported incidence of hearing loss as a function of cochlear dose was used to estimate the probability of occurrence. Results: The integrated brain dose was reduced in all IMPT plans, translating into a gain of 2 IQ points on average for protons for the whole cohort at 5 years post‐treatment. In terms of specific diseases, the gains in IQ ranged from 0.8 points for medulloblastoma, to 2.7 points for craniopharyngioma. Hearing loss probability was evaluated on a per‐ear‐basis and was found to be systematically less for proton versus photon: overall 2.9% versus 7.2%. Conclusions: A method was developed to predict IQ and hearing outcomes in pediatric brain tumor patients on a case‐by‐case basis. A modest gain was systematically observed for proton in all patients. Given the uncertainties within the model used and our reinterpretation, these gains may be underestimated.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".