Amelioration of hypercalcemia by cinacalcet treatment in a subject with relapsing acquired hypocalciuric hypercalcemia
Bibliographic record
Abstract
RATIONALE: Hypocalciuric hypercalcemia is classified as acquired hypocalciuric hypercalcemia (AHH) and familial hypocalciuric hypercalcemia (FHH). While FHH is inherited as an autosomal dominant trait, AHH is one of the rare acquired diseases and is usually treated with prednisolone. Here, we report a case with relapsing AHH which was well controlled with cinacalcet therapy. PATIENT CONCERN: A 68-year-old Japanese man was referred to our institution because of hypercalcemia. Despite such hypercalcemia, he was almost asymptomatic. DIAGNOSTICS: We diagnosed him as AHH due to the following reason. First, the ratio of calcium (Ca)/creatinine clearance was very low which met the criteria. Second, there was no overt family history of hypercalcemia. Third, his serum Ca level was within the normal range 3 years before. Fourth, despite hypercalcemia, he was almost asymptomatic and had no evidence of primary hyperparathyroidism. INTERVENTIONS: Although it is known that steroid therapy is useful for AHH, optimal treatment remains unknown and cinacalcet therapy is very much limited for the treatment of AHH. In this subject, we introduced cinacalcet therapy for the treatment of relapsing AHH. OUTCOMES: Serum Ca and parathyroid hormone levels were normalized after such therapy with cinacalcet. CONCLUSIONS: We should bear in mind that cinacalcet treatment is effective for the treatment of relapsing AHH.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".