P.044 Salvage therapy in recurrent pediatric medulloblastoma: A single centre experience
Bibliographic record
Abstract
Background: Children diagnosed with medulloblastoma (MB) that are refractory to upfront therapy or experience recurrence have very poor prognoses. Reports of phase I and II studies for these children exist, but bear significant treatment related morbidity and mortality. Methods: A retrospective review of children diagnosed with a pediatric MB from 2002-2015 from the McMaster Pediatric Brain Tumour Study Group (PBTSG) captured a number of pediatric recurrent MB. Results: Over the 13-year period, 31 children with a histological diagnosis of MB were treated. At two years, 21 (67.7%) of 31 patients were free of recurrence and 25 (80.6%) survived. Thirteen children had recurrent or treatment refractory MB. mean time to recurrence was 14.6 months. The mean follow-up for survivors of recurrent MB was 4.0 years. In 3 recurrent MB, the disease had significantly progressed and the patients palliated. For the remaining children, therapy offered included surgery, radiation, and chemotherapy agents either in isolation or in varying combinations. Conclusions: Recurrent MB in our cohort carried a poor prognosis despite administration of salvage therapy. Though there is standardization of the upfront treatment exists, we observed great heterogeneity in the treatment of our 13 patients experiencing recurrence. A greater understanding of the biology of recurrent MB has the potential to guide salvage therapy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".