A Lifetime of Hypercalcemia and Hypercalciuria, Finally Explained
Bibliographic record
Abstract
CONTEXT: Hypercalcemia, hypercalciuria, and recurrent nephrolithiasis are all common clinical problems. This case report illustrates a newly described but possibly not uncommon cause of this presenting complex. OBJECTIVE: We report on a patient studied for over 30 years, with the diagnosis finally made with modern biochemical and genetic tools. DESIGN AND SETTING: This study consists of a case report and review of literature conducted in a University Referral Center. PATIENT AND INTERVENTION: A single patient with hypercalcemia, hypercalciuria, and recurrent nephrolithiasis was treated with low-calcium diet, low vitamin D intake, prednisone, and ketoconazole. MAIN OUTCOME MEASURE: We measured the patient's clinical and biochemical response to interventions above. RESULTS: Calcium absorption measured by dual isotope absorptiometry was elevated at 37.4%. Serum levels of 24,25-dihydroxyvitamin D were very low, as measured in two laboratories (0.62 ng/mL [normal, 3.49 ± 1.57], and 0.18 mg/mL). Genetic analysis of CYP24A1 revealed homozygous mutation E143del previously described. The patient's serum calcium and renal function improved markedly on treatment with ketoconazole but not with prednisone. CONCLUSIONS: Chronic hypercalcemia, hypercalciuria, and/or nephrolithiasis may be caused by mutations in CYP24A1 causing failure to metabolize 1,25-dihydroxyvitamin D.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".