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A revised Children's Oncology Group (COG) neuroblastoma risk classification system: Report from the COG biology study ANBL00B1.

2019· article· en· W2947376429 on OpenAlexaff
Meredith S. Irwin, Arlene Naranjo, Susan L. Cohn, Wendy B. London, Julie M. Gastier‐Foster, John M. Maris, Rochelle Bagatell, Julie R. Park, Michael D. Hogarty

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCogMedicineInternal medicineOncologyOverall survivalArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

10012 Background: The COG risk classification system previously used the International Neuroblastoma Staging System (INSS). The pre-treatment INRG staging system (INRGSS) has been adopted internationally, requiring integration of INRGSS with known prognostic biological and clinical characteristics to evaluate outcomes and assess whether this incorporation will require revision to the established COG risk classifier. Methods: 4,037 newly diagnosed neuroblastoma patients were enrolled on COG ANBL00B1 between 2006-2014. Staging per the INSS and INRGSS was determined. Tumor biological and histologic features, including MYCN status [amplified (A) versus not amplified (NA)], ploidy, histology, and segmental chromosome aberrations (SCA) including 1p and 11q LOH, were assessed centrally. Survival analyses were performed to identify independent prognostic factors and to calculate event-free and overall survival (EFS, OS) for combinations of variables used to determine risk group assignments according to COG and INRG classification templates. Results: Using the original COG risk classifier 1,309 low (LR), 992 intermediate (IR) and 1,736 high-risk (HR) patients were identified with 5-year EFS of 91.4±2.1%, 84.3±2.9%, 45.2±3.1%, and OS of 98.1±1.0%, 94.0±1.9%, 54.1±3.0%, respectively. Outcomes based on combinations of clinical and biological prognostic factors were determined and compared for subsets of patients according to the COG (version1) and INRG risk classification systems to develop a revised COG risk classifier that incorporates the INRGSS (version 2, subset shown in table). Conclusions: Use of INRGSS requires a revision to the COG risk classifier. By combining INRGSS and presence of SCA together with age, MYCN status, ploidy, and histology to determine outcome of patients treated with modern era therapies, we developed a revised risk classification system to inform therapy and COG clinical trial eligibility. Clinical trial information: NCT00904241. [Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.116
GPT teacher head0.477
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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