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Record W2015101675 · doi:10.4021/jem.v1i4.51

Rosuvastatin for Tocilizumab, an Interleukin-6 Receptor Antibody-induced Dyslipidemia in a Diabetic Patient Complicated With Rheumatoid Arthritis

2011· article· en· W2015101675 on OpenAlexvenueno aff
Hidekatsu Yanai, Hiroshi Kaneko, Yuji Hirowarari

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2011
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsTocilizumabMedicineRosuvastatinDyslipidemiaRheumatoid arthritisHyperlipidemiaInternal medicineRosuvastatin CalciumGastroenterologyInterleukin 6EndocrinologyPharmacologyDiabetes mellitusCytokine

Abstract

fetched live from OpenAlex

Tocilizumab, which blocks interleukin-6 binding to interleukin-6 receptor, is now approved for the treatment of rheumatoid arthritis (RA). Hyperlipidemia has been reported to be one of the most common adverse effects of tocilizumab, however, the underlying mechanisms remain unknown. To understand lipid metabolism precisely, we measured plasma cytokines, small dense low-density lipoprotein (LDL), oxidized LDL and cholesterol level in each lipoprotein fraction using the high-performance liquid chromatography method before and after the tocilizumab treatment, and also studied the effect of rosuvastatin on tocilizumab-induced dyslipidemia in a diabetic patient complicated with RA. We found that rosuvastatin significantly ameliorated the tocilizumab-induced dyslipidemia. doi:10.4021/jem51w

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.294
Teacher spread0.264 · 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 designCase report
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

Citations0
Published2011
Admission routes1
Has abstractyes

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