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Record W2141362155 · doi:10.1177/0961203306071670

Mycophenolate mofetil as an immunomodulatory silver bullet in atherogenesis?

2006· article· en· W2141362155 on OpenAlexaff
Sander I. van Leuven, JJP Kastelein, Michael R. Hayden, ES Stroes

Bibliographic record

VenueLupus · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAtherosclerosis and Cardiovascular Diseases
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsMedicineMycophenolateAtherosclerotic cardiovascular diseaseImmunologyDiseaseArterial wallInflammationPathologyCardiologyInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Atherosclerotic vascular disease is a chronic disorder of the vasculature with a substantial impact on society. Although the availability of statins has represented an unparalleled improvement in the treatment of patients with such cardiovascular disease, even more effective measures are required to reverse this disorder with a continuously growing incidence. The classification of atherosclerosis as an inflammatory disorder has prompted the hypothesis that immunomodulation could comprise a novel anti-atherosclerotic strategy. Mycophenolate mofetil (MMF) has various anti-atherogenic effects on major components of the atherosclerotic plaque such as T-lymphocytes, monocytes/macrophages and the endothelium. MMF can inhibit leukocyte recruitment to the subendothelium and the subsequent reduced activation of leukocytes will translate into attenuation of subendothelial crosstalk between T-cells and macrophages. This cascade of events will interrupt the self-perpetuating pro-inflammatory environment within the arterial wall, the hallmark of atherosclerotic vascular disease.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations1
Published2006
Admission routes1
Has abstractyes

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