Maintaining excellent outcomes: the impact of age cutoff reclassification on reduced therapy for neuroblastoma patients
Bibliographic record
Abstract
Neuroblastoma, accounting for nearly 12-15% of childhood cancers, is the most prevalent and fatal extracranial solid malignancy affecting children.Nevertheless, despite its low incidence, with approximately 10 cases per million children under 15 years of age (8-10% of the total), neuroblastoma remains a significant clinical concern (1).Neuroblastoma primarily originates in the adrenal gland from neural crest precursor cells, that usually differentiate into adrenal chromaffin and sympathetic ganglion cells.However, it can emerge anywhere along the sympathetic nervous system chain.The exceptional feature of neuroblastoma lies in its diverse clinical behavior, as some tumors regress or mature, while others persist and progress despite intensive multimodal treatments.This variability in behavior closely correlates with a range of clinical and biological characteristics (2).Over the past few decades, extensive efforts have been made to increase the accuracy of the neuroblastoma risk classification system by integrating a variety of clinical and biological parameters.These advancements have facilitated the categorization of patients into low-, intermediate-risk, and high-risk groups.The Children's Oncology Group (COG) applies a set of criteria to categorize patient risk, which includes the patient's age at the time of diagnosis, the disease's extent as per the International Neuroblastoma Staging System (INSS), tumor characteristics determined by the International Neuroblastoma Pathology Classification (INPC) criteria, the MYCN gene status, and the DNA index or tumor cell ploidy (3).Older age has long been associated with poorer outcomes in neuroblastoma since the 1970's.Previous studies indicated that children over 12 months of age at diagnosis had inferior outcomes (4).This evidence was also supported by evidence generated from neuroblastoma mass screening programs conducted in Japan, Quebec and North America, and UK (5).Later on, a retrospective analysis by London et al. from Pediatric Oncology Group (POG) and Children's Cancer Group (CCG) studies revealed that 18 months was a better age cut-off for risk stratification (6).Regarding to the disease stage, Evans et al. described the first staging system for neuroblastoma in 1970, based on both, the site of origin, metastatic spread and the clinical behavior of the tumor (7).Later, an international panel of experts came together to establish a surgical staging system with the aim of facilitating the comparison of outcomes and treatment approaches across different countries.In 1988, the INSS was initially introduced and later revised
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".