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Record W2334960895 · doi:10.3899/jrheum.140817

Assessment of Patients with Takayasu Arteritis in Routine Practice with Indian Takayasu Clinical Activity Score

2015· article· en· W2334960895 on OpenAlexvenueno aff
Fatma Alıbaz-Öner, Sibel Zehra Aydın, Servet Akar, Kenan Aksu, Sevil Kamalı, Eftal Yücel, Ömer Karadağ, Hüseyin Özer, Sedat Kiraz, Fatoş Önen, Murat İnanç, Gökhan Keser, Nurullah Akkoç, Haner Di̇reskeneli̇

Bibliographic record

VenueThe Journal of Rheumatology · 2015
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTakayasu arteritisArteritisTakayasu's arteritisClinical PracticeVascular diseaseInternal medicineClinical diseaseSurgeryRadiologyDiseasePhysical therapyVasculitis

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the Indian Takayasu Clinical Activity Score (ITAS2010) in followup of Takayasu arteritis (TA). METHODS: ITAS2010 forms were filled in prospectively (n = 144). Clinical activity was assessed with physician's global assessment (PGA) and criteria defined by Kerr, et al. RESULTS: ITAS2010 was significantly higher in patients with active disease. Total agreement between ITAS2010 and PGA was 66.4%, and between ITAS2010 and Kerr, et al was 82.8%. During followup, 14 of 15 patients showing vascular progression with imaging were categorized as having inactive disease according to ITAS2010. CONCLUSION: ITAS2010 was discriminatory for activity during the followup, but the agreement between PGA and ITAS2010 was moderate. Future work should include the incorporation of advanced vascular imaging and demonstration of ITAS2010 as a scalable measure and not simply a dichotomous measure of activity/flare versus remission.

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)0.007
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations24
Published2015
Admission routes1
Has abstractyes

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