A systematic review of re-induction chemotherapy for children with relapsed high-risk neuroblastoma
Bibliographic record
Abstract
BACKGROUND: Despite aggressive multimodal therapy, >50% of children with high-risk neuroblastoma (HRNB) relapse. Survival after relapse is rare, and no consensus currently exists on the most effective therapy. OBJECTIVE: To conduct a systematic review of the literature on effectiveness of re-induction chemotherapy in children with relapsed HRNB. METHODS: Database searches were performed to identify studies looking at response to 1st line chemotherapy for children >12 months at diagnosis with first relapse of HRNB. Studies not reporting separate outcomes for HRNB patients or of refractory patients only were excluded. Two independent reviewers extracted the data and assessed study quality using a modified Newcastle-Ottawa tool. RESULTS: Nine studies were identified fitting the inclusion criteria. All except one were single arm cohorts, and two were retrospective database reviews from single centres. One was a multicentre randomised controlled trial. All used a version of the validated International Neuroblastoma Response Criteria with 8 recording best ever response and 1 at a specified time, and 5 had central review. The proportion of relapsed patients varied from 24 to 100% with 30-93% receiving upfront myeloablative therapy. The response rate varied from 6 to 64%; however, because of heterogeneity, studies were not directly comparable, and no single treatment emerged as the most effective re-induction therapy. CONCLUSIONS: To date, there is no clear superior re-induction therapy for 1st relapse of HRNB. Randomised controlled trials with separate arms for relapsed versus refractory disease are needed to determine optimal re-induction chemotherapy to act as a backbone for testing newer targeted agents.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".