Treatment-Related Morbidity in Atypical Teratoid/Rhabdoid Tumor: Multifocal Necrotizing Leukoencephalopathy
Bibliographic record
Abstract
BACKGROUND: Atypical teratoid/rhabdoid tumor (AT/RT) is an aggressive malignant brain tumor that, since it was first identified, has been treated with aggressive treatment regimens, e.g. high-dose chemotherapy with stem cell rescue and early radiotherapy. We reviewed our experience because of concerns with respect to treatment-related toxicity in our patients. METHODS: Seven patients with a median age at presentation of 18 months were diagnosed with AT/RT between 1996 and 2006. Tumor location was supratentorial in 2 patients, in the posterior fossa in 4 and spinal in 1. Gross total resection was performed in 1 patient, subtotal resection in 5 and biopsy only in 1. Adjuvant treatment consisted of chemotherapy and radiotherapy in 5 patients. RESULTS: Median progression-free survival was 4 months, and median overall survival was 7 months. Two children are alive at 44 and 102 months. Significant surgical and chemotherapy-related morbidity was seen. Biopsy-proven multifocal necrotizing leukoencephalopathy (MNL) was seen in one patient who is alive 44 months after diagnosis. Another patient who was thought to have recurrent tumor in the brainstem 9 months after diagnosis had imaging findings compatible with MNL. CONCLUSION: Although improving results are reported for AT/RT using intensive treatment regimens, treatment-related morbidity is considerable in this young patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".