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Record W2103801027 · doi:10.2215/cjn.02160309

Vitamin D and Parathyroid Hormone in General Populations

2009· review· en· W2103801027 on OpenAlexaff
Manraj Johal, Adeera Levin

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2009
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineVitamin D and neurologyParathyroid hormoneDiseasevitamin D deficiencyEpidemiologyPopulationKidney diseasePhysiologyIntensive care medicineOsteoporosisEndocrinologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Vitamin D is now recognized as an important prohormone in health and disease. Its role in immunoregulation and cardiovascular and bone health has become topical in the lay press and the medical press in the past 5 yr. The target audience for this review includes the interested clinician and researchers. The prevalence of chronic kidney disease in the general population has further increased the interest and perhaps the applicability of findings of population studies. The basic physiology of vitamin D and receptor activation and biologic importance is reviewed, as well as various vitamin D analogues and nomenclature. Issues related to measurement of vitamin D and parathyroid hormone have the potential to complicate the clinical use of these tests and should be understood by all clinicians so as to ensure informed decision making and stimulate interest in participation in clinical trials. The epidemiology of vitamin D deficiency and supplementation in association with health status and disease status is reviewed, and issues related to association versus causation are highlighted. Some recommendations for pragmatic approaches and study design are suggested.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.142
GPT teacher head0.476
Teacher spread0.334 · 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 designNot applicable
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

Citations13
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of NephrologySame topicVitamin D Research StudiesFrench-language works237,207