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Record W2114933820 · doi:10.1210/jc.2013-3802

A Lifetime of Hypercalcemia and Hypercalciuria, Finally Explained

2014· review· en· W2114933820 on OpenAlexaff
Thomas P. Jacobs, Martin Kaufman, Glenville Jones, Rajiv Kumar, Karl P. Schlingmann, Sue A. Shapses, John P. Bilezikian

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2014
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsQueen's University
FundersNational Institute on Aging
KeywordsHypercalciuriaCalciumMedicineInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.119
GPT teacher head0.464
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations106
Published2014
Admission routes1
Has abstractyes

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