Medical Management of Primary Hyperparathyroidism: Proceedings of the Fourth International Workshop on the Management of Asymptomatic Primary Hyperparathyroidism
Bibliographic record
Abstract
OBJECTIVE: Asymptomatic primary hyperparathyroidism (PHPT) is a common clinical problem. The only available definitive therapy is parathyroidectomy, which is appropriate to consider in all patients. The purpose of this report is to provide an update on calcium and vitamin D supplementation and medical management for those patients with PHPT who cannot or do not want to undergo surgery. METHODS: Questions were developed by the International Task Force on PHPT. A comprehensive literature search was undertaken, and relevant articles published between 2008 and 2013 were reviewed in detail. The questions were addressed by the panel of experts, and consensus was established at the time of the workshop. CONCLUSIONS: The recommended calcium intake in patients with PHPT should follow guidelines established for all individuals. It is not recommended to limit calcium intake in patients with PHPT who do not undergo surgery. Patients with low serum 25-hydroxyvitamin D should be repleted with doses of vitamin D aiming to bring serum 25-hydroxyvitamin D levels to ≥ 50 nmol/L (20 ng/mL) at a minimum, but a goal of ≥75 nmol/L (30 ng/mL) also is reasonable. Pharmacological approaches are available and should be reserved for those patients in whom it is desirable to lower the serum calcium, increase BMD, or both. For the control of hypercalcemia, cinacalcet is the treatment of choice. Cinacalcet reduces serum calcium concentrations to normal in many cases, but has only a modest effect on serum PTH levels. However, bone mineral density (BMD) does not change. To improve BMD, bisphosphonate therapy is recommended. The best evidence is for the use of alendronate, which improves BMD at the lumbar spine without altering the serum calcium concentration. To reduce the serum calcium and improve BMD, combination therapy with both agents is reasonable, but strong evidence for the efficacy of that approach is lacking.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".