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Record W2331353969 · doi:10.3899/jrheum.100780

Plasma Platelet-derived Microparticles in Patients with Connective Tissue Diseases

2011· article· en· W2331353969 on OpenAlexvenueno aff
Chinami Oyabu, Akio Morinobu, Daisuke Sugiyama, Jun Saegusa, Shino Tanaka, Sahoko Morinobu, Goh Tsuji, Shimpei Kasagi, Seiji Kawano, Shunichi Kumagai

Bibliographic record

VenueThe Journal of Rheumatology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
Fundersnot available
KeywordsPolymyositisMedicineMixed connective tissue diseaseDermatomyositisConnective tissue diseaseConnective tissueSystemic diseasePlateletInternal medicineLupus erythematosusScleroderma (fungus)PathologyImmunologyImmunopathologyAutoimmune diseaseDiseaseAntibody

Abstract

fetched live from OpenAlex

OBJECTIVE: To clarify the role of platelet-derived microparticles (PDMP), which are small vesicles with thrombotic and immunological properties, in systemic lupus erythematosus (SLE), systemic sclerosis (SSc), dermatomyositis/polymyositis (PM/DM), and mixed connective tissue disease (MCTD). METHODS: Plasma levels of PDMP were measured by ELISA, and compared among patients with one of the 4 diseases. Association of PDMP levels with clinical characteristics and medication of the patients was also examined. RESULTS: PDMP levels were higher in patients with MCTD and SSc than in controls. Multiple linear regression analysis revealed that patients with Raynaud's phenomenon (RP) showed higher PDMP levels than those without. PDMP levels in individual patients did not fluctuate significantly over several months. CONCLUSION: PDMP level is associated with MCTD, SSc, and RP, and could be a novel marker for RP.

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)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations20
Published2011
Admission routes1
Has abstractyes

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