Multiple Endocrine Neoplasia and Hyperparathyroid-Jaw Tumor Syndromes: Clinical Features, Genetics, and Surveillance Recommendations in Childhood
Bibliographic record
Abstract
Abstract Children and adolescents who present with neuroendocrine tumors are at extremely high likelihood of having an underlying germline predisposition for the multiple endocrine neoplasia (MEN) syndromes, including MEN1, MEN2A and MEN2B, MEN4, and hyperparathyroid-jaw tumor (HPT-JT) syndromes. Each of these autosomal dominant syndromes results from a specific germline mutation in unique genes: MEN1 is due to pathogenic MEN1 variants (11q13), MEN2A and MEN2B are due to pathogenic RET variants (10q11.21), MEN4 is due to pathogenic CDKN1B variants (12p13.1), and the HPT-JT syndrome is due to pathogenic CDC73 variants (1q25). Although each of these genetic syndromes share the presence of neuroendocrine tumors, each syndrome has a slightly different tumor spectrum with specific surveillance recommendations based upon tumor penetrance, including the age and location for which specific tumor types most commonly present. Although the recommended surveillance strategies for each syndrome contain similar approaches, important differences do exist among them. Therefore, it is important for caregivers of children and adolescents with these syndromes to become familiar with the unique diagnostic criteria for each syndrome, and also to be aware of the specific tumor screening and prophylactic surgery recommendations for each syndrome. Clin Cancer Res; 23(13); e123–e32. ©2017 AACR. See all articles in the online-only CCR Pediatric Oncology Series.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".