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Record W2801998406 · doi:10.14341/osteo20173102-107

Functional hypoparathyroidism secondary to magnesium deficiency in long-term users of proton pump inhibitor

2018· article· en· W2801998406 on OpenAlexaff
L. V. Egshatyan

Bibliographic record

VenueOsteoporosis and Bone Diseases · 2018
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsMcGill-Queen's University Press
Fundersnot available
KeywordsMedicineHypocalcaemiaGERDHypomagnesemiaHypokalemiaInternal medicineProton-pump inhibitorGastroenterologyHypoparathyroidismMagnesium deficiency (plants)Parathyroid hormoneDiseaseRefluxCalciumMagnesium

Abstract

fetched live from OpenAlex

Gastroesophageal reflux disease (GERD) is a gastrointestinal motility disorder that results from the reflux of stomach contents into the esophagus resulting in symptoms or complications. GERD is now widely prevalent around the world, with clear evidence of increasing prevalence in many developing countries. Treatment for most people with GERD includes lifestyle changes and medication. Proton pump inhibitors (PPIs) are a mainstay therapy for all gastric acid-related diseases. Long-term use of PPIs is associated with hypomagnesaemia, hypokalemia, hypocalcaemia, osteoporosis and bone fractures, chronic renal disease, acute renal disease, and other. Clinical concerns arise from a small but growing number of case reports presenting PPI-induced hypomagnesaemia. In 2011 the U.S. Food and Drug Administration is informing the public that prescription PPI may cause low serum magnesium levels if taken for prolonged periods of time.
 In this article, we present the case of a 56-year-old patient with muscle cramps, violation of cardiac rhythm, lethargy and other caused by hypomagnesaemia, hypocalcaemia and hypokalemia with a low parathyroid hormone level while using a PPI. After magnesium repletion abnormalities resolved. A causal relation with PPI use was supported by the recurrence of hypomagnesaemia after re-challenge.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes1
Has abstractyes

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