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Record W2996139546 · doi:10.3747/co.26.5553

A Primer on the Genetics of Medullary Thyroid Cancer

2019· review· en· W2996139546 on OpenAlexaffvenue
Vincent Larouche, Amit Akirov, Carissa M. Thomas, Monika K. Krzyzanowska, Shereen Ezzat

Bibliographic record

VenueCurrent Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsUniversity Health NetworkMcGill UniversityPrincess Margaret Cancer CentreJewish General Hospital
Fundersnot available
KeywordsMultiple endocrine neoplasia type 2Medullary thyroid cancerMedicineGermline mutationGermlineMultiple endocrine neoplasiaThyroidPheochromocytomaThyroid cancerPathologyCancerMedullary cavityRET proto-oncogeneCancer researchMutationGeneticsInternal medicineBiologyGene

Abstract

fetched live from OpenAlex

Medullary thyroid cancer is a rare type of neuroendocrine tumour that arises from the parafollicular cells (C cells) of the thyroid gland. It accounts for 3%–5% of thyroid cancer cases. Close to 25% of cases are familial, and 75% are considered sporadic. Familial cases are associated with a germline RET mutation; 43%–65% of sporadic cases harbour a somatic event in the gene. Germline RET mutations are associated with the autosomal-dominant inherited multiple endocrine neoplasia (MEN) 2A and 2B syndromes and the isolated familial medullary thyroid cancer syndrome. More than 100 RET codon mutations have been reported to date, with genotype–phenotype correlations that include the extent and aggressiveness of the medullary thyroid cancer and the presence of other features of the MEN2 syndromes. The latter include pheochromocytoma–paraganglioma, hyperparathyroidism, cutaneous lichen amyloidosis, and Hirschsprung disease. In this narrative review, we focus on RET proto-oncogene physiology and pathogenesis induced by germline and somatic RET mutations, the genotype–phenotype correlation, and the management and follow-up of patients with germline-mutated medullary thyroid cancer.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.337
GPT teacher head0.507
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations48
Published2019
Admission routes2
Has abstractyes

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