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061. UTILITY OF HIGH DEFINITION MRI IN MONITORING DISEASE ACTIVITY IN TAKAYASU ARTERITIS

2019· article· en· W2933733360 on OpenAlexaffabout
Nikesh Adunuri, Ali Islam, John Butler, William Pavlosky, Lillian Barra

Bibliographic record

VenueLara D. Veeken · 2019
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteVictoria HospitalWestern University
Fundersnot available
KeywordsMedicineTakayasu's arteritisTakayasu arteritisArteritisDisease monitoringRadiologyDiseaseInternal medicineVasculitis

Abstract

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Background: Takayasu’s arteritis (TAK), is a rare large-vessel arteritis typically seen in young females. TAK can cause chronic structural damage such as stenosis and aneurysms, leading to significant morbidity and mortality, even in the absence of symptoms and elevations in acute phase reactants (APR). Currently, there is no gold standard for assessing disease activity in TAK. Given that CTA can pose significant radiation risk to young females, there has been an increased interest in using MRI, which can provide better soft tissue characterization without ionizing radiation exposure. Most studies have investigated the utility of 1.5T MRI in TAK; however, it remains unclear whether this image modality accurately predicts disease progression. We undertook a prospective observational study looking at the utility of high definition 3T MRI in the care of TAK patients to monitor disease activity. Methods: A prospective study of TAK patients meeting ACR criteria was undertaken at St. Joseph’s Health Care (London, Canada). For each patient, baseline demographics, clinical information and clinical activity status were collected at baseline and every 3 to 6 months. MRI of the chest and abdomen was sequentially obtained on each patient 1 to 2 times over the course of follow-up using a 3T PET/MR (Biograph mMR (Siemens, Germany)) with contrast (Gadovist (Bayer, Germany)). Images were assessed by a radiologist blinded to clinical data. Data was collected and analyzed using MS Excel (v.2017). Results: A total of 7 patients were included with a mean age of 29 years (SD 19), mean disease duration of 73 months (SD 66) and mean follow-up of 19 months (SD17). 5/7 patients were on corticosteroids over the course of follow-up with an average dose of 14.9 mg (SD = 19); 3/7 received biologic therapies; 4/7 another immunosuppressant; 1/7 an anti-hypertensive, 2/7 lipid-lowering medications and 5/7 an anti-platelet drug. Figure 1 summarizes the relationship between MRI findings and clinical activity. Patients with findings of active disease on MRI had higher APR. However, patients with clinically inactive disease continued to have evidence of active disease on MRI. Two of these patients had worsening MRI findings on follow-up; one of them developed clinically active disease 3 months later resulting in drug escalation. Graphical representation of APR, Indian Takayasu Arteritis Score 2010 (ITAS) and clinical activity with MRI (active disease defined as wall edema with thickening, worse or new stenosis/ dilation: Active-stable = no change from baseline, Active-worse = new or worse lesions from baseline, Inactive = No evidence of activity), Clinical activity = global clinician evaluation (based on history, physical exam and APR). Conclusion: High definition MRI may detect early active disease and predict disease flare in TAK; however, using it as a routine clinical test needs further exploration. Disclosures: Academic Medical Organization of Southwestern Ontario Opportunities Fund.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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 routes2
Has abstractyes

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