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Record W4403879934 · doi:10.21037/tp-24-319

Improvements in Children’s Oncology Group neuroblastoma risk stratification through a change in age cut-off and use of INRGSS

2024· letter· en· W4403879934 on OpenAlexaff
Wendy B. London, Hannah Bousquet, Meredith S. Irwin, Michael D. Hogarty, Susan L. Cohn

Bibliographic record

VenueTranslational Pediatrics · 2024
Typeletter
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of Health
KeywordsMedicineRisk stratificationNeuroblastomaStratification (seeds)OncologyPediatric oncologyInternal medicineCancer

Abstract

fetched live from OpenAlex

We thank the authors for their supportive comments on our manuscript about the revision to the Children's Oncology Group (COG) neuroblastoma risk stratification based on a change in age cut-off from 12 to 18 months (1,2).The authors have provided a well-described and helpful overview of the application of biomarkers to risk stratify patients, as well as describing treatment and late effects.They have nicely summarized the comparative evidence of our successful reduction of therapy due to the described COG risk stratification changes.In addition to the change in age cut-off, COG risk stratification is anticipated to improve by using the INRGSS (International Neuroblastoma Risk Group Staging System) (3) instead of INSS (International Neuroblastoma Staging System) (4,5).INSS was utilized in 2006 when the change in age cut-off was made, but has since been replaced by the INRGSS in the COG neuroblastoma risk classifier version 2.INRGSS is a pre-surgical staging system which quantifies the disease extent at diagnosis, compared to INSS, which is post-surgical and dependent on surgical discretion.The authors describe the use of the International Neuroblastoma Pathology Classification (INPC) as a biomarker in COG risk stratification.Age is one of the factors used to classify tumors as INPC favorable or unfavorable.In the context of risk stratification, using both age and INPC leads to a duplication of the prognostic contribution of age, i.e., confounding.Further improvements in risk stratification should utilize the underlying components of INPC [histologic category, mitosis-karyorrhexis index (MKI), and grade of differentiation] as separate risk factors, to eliminate this problematic confounding (6).We thank the authors for describing the LEAHRN (Late Effects After High-Risk Neuroblastoma) study, the first comprehensive study specifically focused on late effects in survivors of high-risk neuroblastoma (7).These survivors were diagnosed on/after January 1, 2000, with a minimum of 5 years follow-up after diagnosis.The authors state that survivors were treated between 2000-2006; however, many LEAHRN patients were treated after 2006 with more contemporary therapy.The authors state that "While this re-classification saved children to be exposed to unnecessary treatments, their outcome should remain unaltered".Certainly, it was our hope that their outcome remains 'unaltered', but we should clarify our intent.We hypothesized that despite receiving less intensive therapy, these groups would maintain the same outstanding survival outcomes as they had when they received high-risk

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)0.003
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0050.002

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 head0.051
GPT teacher head0.326
Teacher spread0.274 · 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractno

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