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Record W2163058607 · doi:10.1148/radiol.13121675

MR Imaging of the Spine and Sacroiliac Joints for Spondyloarthritis: Influence on Clinical Diagnostic Confidence and Patient Management

2013· article· en· W2163058607 on OpenAlexaff
Raj Carmona, Srinivasan Harish, Dorota D. Linda, George Ioannidis, Mark Matsos, Nader Khalidi

Bibliographic record

VenueRadiology · 2013
Typearticle
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineSacroiliitisAnkylosing spondylitisSacroiliac jointMagnetic resonance imagingConfidence intervalSpondylitisRadiologyRadicular painBack painMcNemar's testInternal medicineLumbarPathology

Abstract

fetched live from OpenAlex

PURPOSE: To quantify the effect of magnetic resonance (MR) imaging of the spine and sacroiliac joints on clinical diagnostic confidence and to determine if MR imaging affects treatment of patients with axial spondyloarthritis. MATERIALS AND METHODS: This prospective observational study was approved by the research ethics board and included 55 consecutive patients referred by three rheumatologists for MR imaging of the spine and sacroiliac joints. Measures of diagnostic confidence for clinical features (inflammatory back pain, mechanical back pain, muscular back pain, radicular back pain, spondylitis, sacroiliitis, and other) and overall diagnoses were made by using a Likert scale both before and after MR imaging. Proposed treatment was similarly recorded before and after MR imaging interpretation. The McNemar test was performed to determine the change in diagnostic confidence and consequent effect on patient treatment. RESULTS: Diagnostic confidence for specific clinical features improved significantly after MR imaging for inflammatory back pain (14% vs 76%, before vs after; P < .001), mechanical back pain (4% vs 49%, P < .001), spondylitis (7% vs 76%, P < .001) and sacroiliitis (9% vs 87%, P < .001). Confidence for overall diagnoses also improved significantly after MR imaging for ankylosing spondylitis (29% vs 80%, P < .001), undifferentiated spondyloarthritis (58% vs 93%, P < .001) and osteoarthritis (29% vs 64%, P < .001). Of the 23 patients for whom tumor necrosis factor-α inhibitor (TNFi) therapy was recommended before MR imaging, 12 (52%) were prescribed TNFi therapy after MR imaging. Of the 32 patients for whom TNFi therapy was not recommended before MR imaging, 10 (31%) patients were prescribed TNFi therapy after MR imaging. Overall, 22 (40%) patients had a change in treatment recommendation regarding TNFi therapy after MR imaging. CONCLUSION: MR imaging of the spine and sacroiliac joints significantly influences the diagnostic confidence of rheumatologists regarding clinical features and overall diagnoses of axial spondyloarthritis, and consequently significantly affects treatment plans.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.154
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.288
Teacher spread0.275 · 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 teacher head, 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

Citations33
Published2013
Admission routes1
Has abstractyes

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