Predictors of differential response to induction chemotherapy in high-risk neuroblastoma: A report from the Children's Oncology Group (COG).
Bibliographic record
Abstract
10532 Background: Induction chemotherapy plays an important role in the management of patients with high-risk neuroblastoma. Predictors of response to Induction therapy itself are largely lacking. We sought to describe clinical and biological features associated with differential response to Induction. Methods: Patients from the following COG high-risk trials with at least one disease evaluation during Induction were included: A3973; ANBL02P1; ANBL0532; and ANBL12P1. Response at end-Induction was evaluated by the 1993 International Neuroblastoma Response Criteria. The primary endpoint was partial response (PR) or better. A series of univariate analyses (Fisher's exact or chi-squared tests) were performed to compare response as a function of clinical or biologic predictor variables. For each predictor variable, the Holm-Bonferroni method was used to correct for multiple testing, using an overall α=0.05. A multivariate logistic regression model using significant predictors from univariate analyses was constructed to model PR or better. Results: The analytic cohort included 1,242 patients (79.8% with PR or better; 20.8% with CR; 9.1% with PD). Baseline factors significantly associated with a PR or better included age <18 months (87.4% with PR or better vs. 78.7% if older; p=0.0103), age <5 years (82.0% vs. 70.6% if older; p<0.0001), INSS
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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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 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".