Factors Influencing Survival in Children With Recurrent Neuroblastoma
Bibliographic record
Abstract
Despite advances in multimodal therapy for neuroblastoma, survival from advanced disease remains poor. Children are now offered a wide variety of salvage regimens following relapse. A retrospective review was performed on 31 patients with recurrent neuroblastoma treated at one institution between 1995 and 2001. At initial diagnosis, 27 patients had metastatic disease and 11 had N-myc amplification (NMA). The median time to recurrence from diagnosis was 16.1 months. Seventeen patients received salvage therapy, with a median of three salvage regimens per patient. The median survival time from relapse was 8.4 months. The median survival time was significantly shorter for recurrence less than 6 months after stem cell transplantation (2.9 vs. 13.3 months; P = 0.003) and for patients with NMA (2.7 vs. 15.1 months; P < 0.0001). Overall, salvage therapy led to a significantly longer median survival time (22.4 vs. 3.3 months; P = 0.0003); however, salvage therapy extended the median survival time only from 2.2 to 3.2 months for patients with NMA and from 0.7 to 5.8 months for patients with early relapse after stem cell transplantation. Multiple salvage regimens prolong survival significantly, especially for patients with no NMA and for relapses more than 6 months after stem cell transplantation, but the long-term disease-free survival after recurrent disease remains dismal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".