MétaCan
Menu
Back to cohort
Record W2890751747 · doi:10.1101/mcs.a002568

Application of genomics to identify therapeutic targets in recurrent pediatric papillary thyroid carcinoma

2018· article· en· W2890751747 on OpenAlexafffund
Rebecca Ronsley, Shahrad R. Rassekh, Yaoqing Shen, Anna F. Lee, Colleen Jantzen, Jessica Halparin, Catherine M. Albert, Douglas M. Hawkins, Shazhan Amed, Ralph R. Rothstein, Andrew J. Mungall, David Dix, Geoffrey K. Blair, Helen Nadel, Steven J.M. Jones, Janessa Laskin, Marco A. Marra, Rebecca Deyell

Bibliographic record

VenueMolecular Case Studies · 2018
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer AgencyBC Children's HospitalUniversity of British Columbia
FundersCommon FundNIH Office of the DirectorNational Human Genome Research InstituteLoxo OncologyNational Cancer InstituteBC Cancer FoundationNational Institutes of HealthGenome British ColumbiaGenome Canada
KeywordsThyroid carcinomaMedicinePapillary thyroid cancerThyroid cancerThyroidFusion geneCancer researchOncologyInternal medicineBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Children with papillary thyroid carcinoma (PTC) may relapse despite response to radioactive iodine (RAI). Two children with multiply relapsed PTC underwent whole-genome and transcriptome sequencing. A TPM3-NTRK1 fusion was identified in one tumor, with outlier NTRK1 expression compared to the TCGA thyroid cancer compendium and to Illumina BodyMap normal thyroid. This patient demonstrated resolution of multiple pulmonary nodules without toxicity on oral TRK inhibitor therapy. A RET fusion was identified in the second tumor, another potentially actionable finding. Identification of oncogenic drivers in recurrent pediatric PTC may facilitate targeted therapy while avoiding repeated RAI.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.339
Teacher spread0.315 · 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 designCase report
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

Citations16
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueMolecular Case StudiesSame topicThyroid Cancer Diagnosis and TreatmentFrench-language works237,207