MétaCan
Menu
Back to cohort
Record W1963696301 · doi:10.3899/jrheum.130154

Outcomes of Patients with Takayasu Arteritis Treated with Infliximab

2013· letter· en· W1963696301 on OpenAlexvenueno aff
Fabio Bonilla‐Abadía, Carlos A. Cañas

Bibliographic record

VenueThe Journal of Rheumatology · 2013
Typeletter
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineErythrocyte sedimentation rateInfliximabStenosisInternal medicineClaudicationArteritisChest painPalpitationsTakayasu arteritisCardiologySurgeryMagnetic resonance angiographyRheumatologyVasculitisRadiologyVascular diseaseMagnetic resonance imagingTumor necrosis factor alpha

Abstract

fetched live from OpenAlex

To the Editor: Takayasu arteritis (TA) is a chronic vasculitis of unknown etiology involving the aorta and its main branches. Progression may lead to stenosis or formation of aneurysms1. Current therapy is based on corticosteroids and immunosuppressive agents. Treatment with anti-tumor necrosis factor-α (anti-TNF-α) has shown efficacy, as well as unfavorable outcomes2,3,4,5,6,7. From a cohort of 14 patients with TA refractory to conventional treatments, we treated 3 with infliximab (IFX), and describe the outcome. A 21-year-old woman was admitted because of 6 months of progressive chest pain, palpitations, and claudication in the left upper limb. Examination showed differential blood pressure between arms, decreased pulses in the left arm, bilateral carotid, and mitral murmur; erythrocyte sedimentation (ESR) rate was 54 mm/h and C-reactive protein (CRP) was 3 mg/dl. Angiography study showed stenosis in the thoracic aorta and supraaortic and renal vessels compatible with a diagnosis … Address correspondence to Dr. Cañas; E-mail: cacd12{at}hotmail.com

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)0.005
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.208
Teacher spread0.202 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of RheumatologySame topicVasculitis and related conditionsFrench-language works237,207