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Record W2947054215 · doi:10.1002/gcc.22782

Molecular characteristics of poorly differentiated chordoma

2019· article· en· W2947054215 on OpenAlexaff
Angela Shih, Ivan Chebib, Vikram Deshpande, Brendan C. Dickson, A. John Iafrate, G. Petur Nielsen

Bibliographic record

VenueGenes Chromosomes and Cancer · 2019
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsChordomaBiologyMultiplexGeneticsMutationCancer researchGenePathologyMedicine

Abstract

fetched live from OpenAlex

Pediatric poorly differentiated chordoma is a subtype of chordoma with a much more aggressive clinical course and has been characterized by loss of SMARCB1. This study characterizes the molecular features of these tumors in comparison to conventional chordoma. A search of records between 1990 and 2017 at Massachusetts General Hospital identified two patients with sufficient excess tissue for molecular analysis and a third patient diagnosed with a highly cellular conventional chordoma. The three tumors were sent for array comparative genomic hybridization for genome-wide copy number variants; multiplex PCR for single-nucleotide variants; and RNA-sequencing for fusions. Poorly differentiated chordoma showed chromosome 22q loss, including SMARCB1, with no identifiable mutations on multiplex PCR. The cellular conventional chordoma showed a complex pattern of chromosomal gains and losses involving 12 chromosomes, and an RB1 mutation at low allelic frequency. RNA-Seq identified no disease-defining gene fusion events. Poorly differentiated chordoma appears to represent a distinct type of tumor that is genetically unrelated to conventional chordoma. Recognition of this subtype is important because these malignancies should be treated aggressively with multimodality therapy, and possibly targeted therapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.098
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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 teacher head, 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

Citations30
Published2019
Admission routes1
Has abstractyes

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