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Record W3032997756 · doi:10.1530/erc-20-0201

HEREDITARY ENDOCRINE TUMOURS: CURRENT STATE-OF-THE-ART AND RESEARCH OPPORTUNITIES: History of the multiple endocrine neoplasia workshops and overview of MEN2019

2020· editorial· en· W3032997756 on OpenAlexaboutno aff
Elizabeth G. Grubbs, Daniel M. Halperin, Steven G. Waguespack, Robert F. Gagel

Bibliographic record

VenueEndocrine Related Cancer · 2020
Typeeditorial
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsPheochromocytomaMultiple endocrine neoplasiaMEN1MedicineMultiple endocrine neoplasia type 2Thyroid carcinomaEndocrine systemCancerThyroid cancerThyroidInternal medicineHormoneBiologyGeneGermline mutationGenetics

Abstract

fetched live from OpenAlex

The multiple endocrine neoplasia (MEN) workshops had their beginnings at Queen's University in Kingston, Ontario in June 1984. This initial meeting brought clinicians and scientists together to focus on mapping the gene for multiple endocrine neoplasia type 2 (MEN2). These efforts culminated in the identification of the RET protooncogene as the causative gene a decade later. Over the next 35 years there were a total of 16 international workshops focused on the several MEN syndromes. Importantly, these workshops were instrumental in efforts to define the molecular basis for multiple endocrine neoplasia type 1 (MEN1), MEN2, von Hippel-Lindau disease (VHL), Carney Complex, hereditary pheochromocytoma and hyperparathyroidism. In this same spirit some 150 scientists and clinicians met at MD Anderson Cancer Center, 27-29 March 2019, for the 16th International Workshop on Multiple Endocrine Neoplasia (MEN2019). Appropriate to its location in a cancer centre, the workshop focused on important issues in the causation and treatment of malignant aspects of the MEN syndromes: medullary thyroid carcinoma, pancreatic neuroendocrine tumours, malignant pheochromocytoma and parathyroid carcinoma. Workshops at the meeting focused on a better understanding of how the identified molecular defects in these genetic syndromes lead to transformation, how to apply targeted kinase inhibitors and immunotherapy to treat these tumours and important clinical management issues. This issue of Endocrine-Related Cancer describes these discussions and recommendations.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.005
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 head0.096
GPT teacher head0.381
Teacher spread0.284 · 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.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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