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Immunomodulating effects of vitamin D analogs in hemodialysis patients

2005· review· en· W2067844651 on OpenAlexvenueno aff
Eric Seibert, Nathan W. Levin, Martin K. Kuhlmann

Bibliographic record

VenueHemodialysis International · 2005
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsParicalcitolMedicineVitamin D and neurologySecondary hyperparathyroidismCalcitriolHemodialysisCalcitriol receptorParathyroid hormoneDialysisInflammationPopulationHyperparathyroidismDiseaseHomeostasisInternal medicineEndocrinologyCalcium

Abstract

fetched live from OpenAlex

Apart from its well-known functions in calcium homeostasis and parathyroid hormone regulation, 1,25-(OH)2D3 and its synthetic analogs are being increasingly recognized for their potent antiproliferative, pro-differentiative and immunomodulating activities. The effects of these drugs are exerted either via vitamin D receptor-dependent genomic, or cell-surface receptor-mediated, non-genomic pathways. Several vitamin D analogs with fewer hypercalcemic side effects have been developed for use in secondary hyperparathyroidism. These analogs may potentially improve treatment of autoimmune disorders and graft rejection, and in dialysis patients may open a new opportunity for amelioration of the chronic inflammatory status. It has recently been shown that hemodialysis (HD) patients treated with paricalcitol have lower total and cardiovascular mortality and morbidity rates and experience improved hospitalization outcomes compared with HD patients treated with calcitriol, suggesting a potential beneficial effect in chronic inflammation and the development of cardiovascular disease. Specific studies on the immunomodulating effects of vitamin D are needed in the HD population.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.351
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 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

Citations27
Published2005
Admission routes1
Has abstractyes

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