65 YEARS OF THE DOUBLE HELIX: Endocrine tumour syndromes in children and adolescents
Bibliographic record
Abstract
As medicine is poised to be transformed by incorporating genetic data in its daily practice, it is essential that clinicians familiarise themselves with the information that is now available from more than 50 years of genetic discoveries that continue unabated and increase by the day. Endocrinology has always stood at the forefront of what is called today ‘precision medicine’: genetic disorders of the pituitary and the adrenal glands were among the first to be molecularly elucidated in the 1980s. The discovery of two endocrine-related genes, GNAS and RET , both identified in the late 1980s, contributed greatly in the understanding of cancer and its progression. The use of RET mutation testing for the management of medullary thyroid cancer was among the first and one of most successful applications of genetics in informing clinical decisions in an individualised manner, in this case by preventing cancer or guiding the choice of tyrosine kinase inhibitors in cancer treatment. New information emerges every day in the genetics or system biology of endocrine disorders. This review goes over most of these discoveries and the known endocrine tumour syndromes. We cover key genetic developments for each disease and provide information that can be used by the clinician in daily practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".