HEREDITARY ENDOCRINE TUMOURS: CURRENT STATE-OF-THE-ART AND RESEARCH OPPORTUNITIES: History of the multiple endocrine neoplasia workshops and overview of MEN2019
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".