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TP53 Germline Pathogenic Variant Frequency in Anaplastic Rhabdomyosarcoma: A Children’s Oncology Group Report

2022· preprint· en· W4223998631 on OpenAlexaff
Douglas Fair, Luke Maese, Yueh‐Yun Chi, Minjie Li, Douglas S. Hawkins, Rajkumar Venkatramani, Erin R. Rudzinski, David M. Parham, Lisa A. Teot, David Malkin, Sharon E. Plon, He Li, Aniko Sabo, Philip J. Lupo, Joshua D. Schiffman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersHyundai Hope On WheelsNational Cancer InstituteChildren’s Oncology GroupHope FoundationSt. Baldrick's FoundationHuntsman Cancer InstituteCancer Prevention and Research Institute of Texas
KeywordsGermlineAnaplasiaRhabdomyosarcomaGermline mutationOncologyCogMedicineInternal medicineLi–Fraumeni syndromeSarcomaGeneticsBiologyMutationPathologyGene

Abstract

fetched live from OpenAlex

Rhabdomyosarcoma (RMS) is a well-described cancer in Li-Fraumeni Syndrome (LFS), resulting from germline TP53 pathogenic variants (PVs). RMS exhibiting anaplasia (anRMS) have been associated with a high rate of germline TP53 PVs. This study provides an updated estimate of the prevalence of TP53 germline PVs from a large cohort of patients (n=239) enrolled in five Children’s Oncology Group (COG) clinical trials. Although the prevalence of germline TP53 PVs in anRMS patients in this series is much lower than previously reported, this prevalence remains significantly elevated. Germline genetic evaluation for TP53 PVs should be strongly considered in patients with anRMS.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.284
Teacher spread0.269 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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