Clinicopathological correlates of hyperparathyroidism
Bibliographic record
Abstract
Hyperparathyroidism is a common endocrine disorder with potential complications on the skeletal, renal, neurocognitive and cardiovascular systems. While most cases (95%) occur sporadically, about 5% are associated with a hereditary syndrome: multiple endocrine neoplasia syndromes (MEN-1, MEN-2A, MEN-4), hyperparathyroidism-jaw tumour syndrome (HPT-JT), familial hypocalciuric hypercalcaemia (FHH-1, FHH-2, FHH-3), familial hypercalciuric hypercalcaemia, neonatal severe hyperparathyroidism and isolated familial hyperparathyroidism. Recently, molecular mechanisms underlying possible tumour suppressor genes (MEN1, CDC73/HRPT2, CDKIs, APC, SFRPs, GSK3β, RASSF1A, HIC1, RIZ1, WT1, CaSR, GNA11, AP2S1) and proto-oncogenes (CCND1/PRAD1, RET, ZFX, CTNNB1, EZH2) have been uncovered in the pathogenesis of hyperparathyroidism. While bi-allelic inactivation of CDC73/HRPT2 seems unique to parathyroid malignancy, aberrant activation of cyclin D1 and Wnt/β-catenin signalling has been reported in benign and malignant parathyroid tumours. Clinicopathological correlates of primary hyperparathyroidism include parathyroid adenoma (80-85%), hyperplasia (10-15%) and carcinoma (<1-5%). Secondary hyperparathyroidism generally presents with diffuse parathyroid hyperplasia, whereas tertiary hyperparathyroidism reflects the emergence of autonomous parathyroid hormone (PTH)-producing neoplasm(s) from secondary parathyroid hyperplasia. Surgical resection of abnormal parathyroid tissue remains the only curative treatment in primary hyperparathyroidism, and parathyroidectomy specimens are frequently encountered in this setting. Clinical and biochemical features, including intraoperative PTH levels, number, weight and size of the affected parathyroid gland(s), are crucial parameters to consider when rendering an accurate diagnosis of parathyroid proliferations. This review provides an update on the expanding knowledge of hyperparathyroidism and highlights the clinicopathological correlations of this prevalent disease.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".