Impact of induction chemotherapy, hyperfractionated accelerated radiotherapy and high‐dose thiotepa on brain volume loss and functional status of children with primitive neuroectodermal tumour
Bibliographic record
Abstract
BACKGROUND: The introduction of aggressive chemo-radiotherapy regimens has improved overall survival in children with primitive neuroectodermal tumours (PNET). However, these combinations may result in neurotoxicity. Previously reported magnetic resonance imaging abnormalities in children receiving intensive sequential chemotherapy, hyperfractionated accelerated radiotherapy (HART) and high-dose thiotepa prompted us to investigate the degree of brain volume loss and patients' functional status after therapy. METHODS: We retrospectively reviewed clinico-radiological data of children with PNET treated in this way at our centre. RESULTS: We studied 14 children treated between December 2009 and April 2013. Data were not complete for one child. Performance status was severely restricted in four children, and mildly to moderately impaired in 7 of the 13 children. Eleven of 13 children showed mild-to-severe generalised neuroparenchymal atrophy, in 7 of whom neuroparenchymal volume loss was moderate to severe. Of these seven, six had received high-dose thiotepa. There was no correlation between brain volume loss and Lansky performance status. However, unexpected neurotoxicities, such as symptoms of transverse myelitis, were observed. CONCLUSION: Measurement of brain volume loss in patients treated with HART and high-dose thiotepa may not be sufficient to predict function. However, correlation of brain volume loss due to late neurotoxicity with performance decline may be more obvious over longer period of follow-up. The combination of HART and myeloablative courses of thiotepa is associated with severe neurotoxicity and subsequent decline in performance status in a significant proportion of patients.
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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 | 0.000 | 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) | 0.000 | 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".