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084. DISCOVERY AND VALIDATION OF A NOVEL ANGIOGRAPHIC CLASSIFICATION SCHEME IN TAKAYASU’S ARTERITIS

2019· article· en· W2928276802 on OpenAlexaff
Katherine Gribbons, Ruchika Goel, David Cuthbertson, Simon Carette, Gary S. Hoffman, George Joseph, Nader Khalidi, Curry L. Koening, Carol A. Langford, Kathleen Maksimowicz‐McKinnon, Carol A. McAlear, Paul A. Monach, Larry W. Moreland, Aswin Nair, Christian Pagnoux, Raheesh Ravindran, Philip Seo, Antoine G. Sreih, Kenneth J. Warrington, Steven R. Ytterberg, Peter A. Merkel, Debashish Danda, Peter C. Grayson

Bibliographic record

VenueLara D. Veeken · 2019
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsMount Sinai HospitalSt. Joseph's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineTakayasu arteritisClassification schemeArteritisTakayasu's arteritisRadiologyVasculitisInternal medicineMachine learningDiseaseComputer science

Abstract

fetched live from OpenAlex

Background: Takayasu’s arteritis (TAK) is characterized by variable patterns of damage throughout the large arteries. Methods: Data was used from patients with TAK from four independent cohorts: one in India and three in North America (NA). All patients underwent whole-body angiography of the aorta and branch vessels, with categorization of involvement (stenosis, occlusion, or aneurysm) in 13 arterial territories. K-means cluster analysis was performed to identify subgroups of patients based on pattern of angiographic involvement. Cluster groups were identified in the Indian cohort and independently validated in the NA cohorts. Results: 581 and 225 patients with TAK were included from the Indian and NA cohorts, respectively. Three distinct clusters were identified in the Indian cohort and validated in the NA cohorts. Patients in Cluster 1 had significantly more disease in the abdominal aorta, renal, and mesenteric arteries (p < 0.01). Patients in Cluster 2 had significantly more bilateral disease in the carotid and subclavian arteries (p < 0.01). Compared to Clusters 1 and 2, patients in Cluster 3 had asymmetric disease with fewer involved territories (p < 0.01). In the Indian and the NA cohorts, patients in Clusters 1 and 2 compared to Cluster 3 were more likely to have arterial occlusions (58% vs 82% vs 37%; p < 0.01) and a history of tuberculosis (8% vs 10% vs 3%; p = 0.03). Disease onset in childhood (28% vs 16% vs 19%; p < 0.01) and hypertension (71% vs 42% vs 39%; p < 0.01) were more common in Cluster 1. Stroke (0% vs 22% vs 5%; p = 0.03), vision loss (0% vs 33% vs 6%; p = 0.01), carotidynia (3% vs 26% vs 9%; p = 0.01) and persistent disease activity (46% vs 59% vs 44%; p = 0.02) were significantly more prevalent in Cluster 2. Conclusion: This large study in TAK identified and validated three novel subsets of patients based on patterns of arterial disease. Angiographic-based disease classification may help identify causal disease factors and enable stratified clinical decision making in this complex, clinically heterogeneous disease. Disclosures: Intramural NIAMS Program and the VCRC Abstract 084 Table 1: <0.01 Temporal artery examination abnormality defined as cord-like, tender, or absent/diminished pulse. Pulse abnorn1ality defined as reduced or absent pulse. LV involvement defined as presence of stenosis, occlusion, aneurysm, FDG uptake, or halo sign on angiogram (MR, CT, catheter), PET or ultrasound in the aorta or branch arteries (excluding temporal artery).

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)0.005
metaresearch head score (Gemma)0.010
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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