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Record W2037455021 · doi:10.5414/cn107355

Normocalcemic hyperparathyroidism in patients with recurrent kidney stones: a disease entity or vitamin D deficiency?

2012· article· en· W2037455021 on OpenAlexaff
Chrysoula Pipili, Nigar Sekecioglu, Dimitrios G. Oreopoulos

Bibliographic record

VenueClinical Nephrology · 2012
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineVitamin D and neurologyParathyroid hormonevitamin D deficiencyHyperparathyroidismInternal medicineKidney diseaseProspective cohort studyUrologyUrinary systemGastroenterologyCohortEndocrinologyCalcium

Abstract

fetched live from OpenAlex

This retrospective data analysis was undertaken to examine the biochemical differences between renal stone formers with normocalcemic hyperparathyroidism (NHPT) and those with normal parathyroid hormone (PTH) levels. Our goal was to ascertain whether 25-hydroxyvitamin D (25(OH)D) status related to PTH levels in this patient cohort. Our findings among 74 patients with NHPT indicate that stone formers with NHPT had significantly lower 25(OH)D levels compared to 192 controls (p = 0.0001) and that 25(OH)D is positively correlated with 1,25-dihydroxyvitamin D values (R = 0.736, p = 0.015). Sequential measurements (after 3 - 5 years), among 11 patients with NHPT who did not receive vitamin D (VitD) preparations, showed a significant increase in urinary calcium (3.43 ± 1.96 vs. 5.72 ± 3.95, p = 0.0426) without a significant change in PTH levels. VitD supplementation, to 3 patients resulted in significant PTH decrease (11.8 ± 1.8 vs. 9.8 ± 1.3, p = 0.003). Prospective studies are needed to confirm the role of vitamin supplementation in renal stone formers with NHPT.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.366
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2012
Admission routes1
Has abstractyes

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