Current and Future Strategies for Relapsed Neuroblastoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More than half of the patients with high-risk neuroblastoma (NB) will relapse despite intensive multimodal therapy, with an additional 10% to 20% refractory to induction chemotherapy. Management of these patients is challenging, given disease heterogeneity, resistance, and organ toxicity including poor hematological reserve. This review will discuss the current treatment options and consider novel therapies on the horizon. Cytotoxic chemotherapy regimens for relapse and refractory NB typically center on the use of the camptothecins, topotecan and irinotecan, in combination with agents such as cyclophosphamide and temozolomide, with objective responses but poor long-term survival. I-meta-iodobenzylguanidine therapy is also effective for relapsed patients with meta-iodobenzylguanidine-avid disease, with objective responses in a third of cases. Immunotherapy with anti-GD2 has recently been incorporated into upfront therapy, but its role in the relapse setting remains uncertain, especially for patients with bulky disease. Future cell-based immunotherapies and other approaches may be able to overcome this limitation. Finally, many novel molecularly targeted agents are in development, some of which show specific promise for NB. Successful incorporation of these agents will require combinations with conventional cytotoxic chemotherapies, as well as the development of predictive biomarkers, to ultimately personalize approaches to patients with "targetable" molecular abnormalities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it