A subset of image‐defined risk factors predict completeness of resection in children with high‐risk neuroblastoma: An international multicenter study
Bibliographic record
Abstract
BACKGROUND: Image-defined risk factors (IDRFs) were promulgated for predicting the feasibility and safety of complete primary tumor resection in children with neuroblastoma (NB). There is limited understanding of the impact of individual IDRFs on resectability of the primary tumor or patient outcomes. A multicenter database of patients with high-risk NB was interrogated to answer this question. DESIGN/METHODS: Patients with high-risk NB (age <20 years) were eligible if cross-sectional imaging was performed at least twice prior to resection. IDRFs and primary tumor measurements were recorded for each imaging study. Extent of resection was determined from operative reports. RESULTS: There were 211 of 229 patients with IDRFs at diagnosis, and 171 patients with IDRFs present pre-surgery. A ≥90% resection was significantly more likely in the absence of tumor invading or encasing the porta hepatis, hepatoduodenal ligament, superior mesenteric artery (SMA), renal pedicles, abdominal aorta/inferior vena cava (IVC), iliac vessels, and/or diaphragm at diagnosis or an overlapping subset of IDRFs (except diaphragm) at pre-surgery. There were no significant differences in event-free survival (EFS) and overall survival (OS) when patients were stratified by the presence versus absence of any IDRF either at diagnosis or pre-surgery. CONCLUSION: Two distinct but overlapping subsets of IDRFs present either at diagnosis or after induction chemotherapy significantly influence the probability of a complete resection in children with high-risk NB. The presence of IDRFs was not associated with significant differences in OS or EFS in this cohort.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".