Updated Recommendations for Pediatric Surveillance in Hereditary Endocrine Neoplasia Syndromes: Multiple Endocrine Neoplasias, Hyperparathyroidism–Jaw Tumor Syndrome, and Carney Complex
Bibliographic record
Abstract
Hereditary endocrine neoplasia syndromes comprise multiple entities associated with an increased risk for the development of endocrine and nonendocrine neoplasms and other systemic manifestations. These syndromes typically demonstrate autosomal dominant inheritance, and each syndrome is associated with a unique genetic predisposition to a distinct spectrum of tumor susceptibility. Moreover, genotype-phenotype associations within each syndrome may affect the spectrum, penetrance, and age of onset of associated tumors. As many endocrine tumors are benign and/or indolent, a careful approach to monitoring is necessary, wherein the nature, timing of initiation, and frequency of presymptomatic surveillance balance the goal of detecting tumors at a point in which intervention would limit tumor-associated morbidity against the physical, emotional, and financial burdens of surveillance. In this study, we summarize changes in knowledge and practice recommendations related to children with multiple endocrine neoplasia syndromes (types 1, 2A, 2B, 4, and 5), hyperparathyroidism-jaw tumor syndrome, and Carney complex since an initial summary in 2017. These updates reflect the evolving understanding of these complex genetic disorders and aim to improve patient care and outcomes.
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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 | 0.010 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".