Cinacalcet Reduces Serum Calcium Concentrations in Patients with Intractable Primary Hyperparathyroidism
Bibliographic record
Abstract
CONTEXT: Patients with persistent primary hyperparathyroidism (PHPT) after parathyroidectomy or with contraindications to parathyroidectomy often require chronic treatment for hypercalcemia. OBJECTIVE: The objective of the study was to assess the ability of the calcimimetic, cinacalcet, to reduce serum calcium in patients with intractable PHPT. DESIGN: This was an open-label, single-arm study comprising a titration phase of variable duration (2-16 wk) and a maintenance phase of up to 136 wk. SETTING: The study was conducted at 23 centers in Europe, the United States, and Canada. PATIENTS: The study included 17 patients with intractable PHPT and serum calcium greater than 12.5 mg/dl (3.1 mmol/liter). INTERVENTION: During the titration phase, cinacalcet dosages were titrated every 2 wk (30 mg twice daily to 90 mg four times daily) for 16 wk until serum calcium was 10 mg/dl or less (2.5 mmol/liter). If serum calcium increased during the maintenance phase, additional increases in the cinacalcet dose were permitted. MAIN OUTCOME MEASURE: The primary end point was the proportion of patients experiencing a reduction in serum calcium of 1 mg/dl or greater (0.25 mmol/liter) at the end of the titration phase. RESULTS: Mean +/- sd baseline serum calcium was 12.7 +/- 0.8 mg/dl (3.2 +/- 0.2 mmol/liter). At the end of titration, a 1 mg/dl or greater reduction in serum calcium was achieved in 15 patients (88%). Fifteen patients (88%) experienced treatment-related adverse events, none of which were serious. The most common adverse events were nausea, vomiting, and paresthesias. CONCLUSIONS: In patients with intractable PHPT, cinacalcet reduces serum calcium, is generally well tolerated, and has the potential to fulfill an unmet medical need.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".