Hypoparathyroidism: Genetics and Diagnosis
Bibliographic record
Abstract
This narrative report summarizes diagnostic criteria for hypoparathyroidism and describes the clinical presentation and underlying genetic causes of the nonsurgical forms. We conducted a comprehensive literature search from January 2000 to January 2021 and included landmark articles before 2000, presenting a comprehensive update of these topics and suggesting a research agenda to improve diagnosis and, eventually, the prognosis of the disease. Hypoparathyroidism, which is characterized by insufficient secretion of parathyroid hormone (PTH) leading to hypocalcemia, is diagnosed on biochemical grounds. Low albumin-adjusted calcium or ionized calcium with concurrent inappropriately low serum PTH concentration are the hallmarks of the disease. In this review, we discuss the characteristics and pitfalls in measuring calcium and PTH. We also undertook a systematic review addressing the utility of measuring calcium and PTH within 24 hours after total thyroidectomy to predict long-term hypoparathyroidism. A summary of the findings is presented here; results of the detailed systematic review are published separately in this issue of JBMR. Several genetic disorders can present with hypoparathyroidism, either as an isolated disease or as part of a syndrome. A positive family history and, in the case of complex diseases, characteristic comorbidities raise the clinical suspicion of a genetic disorder. In addition to these disorders' phenotypic characteristics, which include autoimmune diseases, we discuss approaches for the genetic diagnosis. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".