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Record W4405560680 · doi:10.1142/s2661341724500044

Comparing the Accuracy and Reliability of Detecting the Intensity of Spinal Inflammation on STIR Sequence with Apparent Diffusion Coefficient Values in Axial Spondyloarthritis

2024· article· en· W4405560680 on OpenAlexaboutno aff
Ho Yin Chung, TT Cheung, Vince Wing Hang Lau, Kam Ho Lee, King‐Pui Florence Chan

Bibliographic record

VenueJournal of Clinical Rheumatology and Immunology · 2024
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsEffective diffusion coefficientAxial spondyloarthritisMagnetic resonance imagingNuclear medicineMedicineDiffusion MRIIntensity (physics)MathematicsNuclear magnetic resonanceRadiologyPhysics

Abstract

fetched live from OpenAlex

Objective: To compare the accuracy and reliability of detecting the intensity of spinal inflammation on short tau inversion recovery (STIR) with the apparent diffusion coefficient (ADC) values of the active magnetic resonance imaging (MRI) lesions in axial spondyloarthritis (axSpA). Materials and methods: Fifty active lesions in the STIR sequence of spinal MRI were identified. With reference to sites of active lesions in STIR, the corresponding region of interest (ROI) on the ADC map was drawn to determine the maximum ADC (ADC[Formula: see text]), mean ADC (ADC[Formula: see text]), normalized maximum (nADC[Formula: see text]), and mean (nADC[Formula: see text]). Four independent readers scored the identified active lesions as “intense” or “non-intense” according to the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI index. They were compared to various ADC parameters for assessment of accuracy and reliability. Regression analyses were used to adjust potential factors that could affect ADC. Results: Significant differences were found in ADC [Formula: see text] between “intense” and “non-intense” lesions scored by three of the four readers (1,405.7 ± 271.4 vs. 1,165.8 ± 223.8, [Formula: see text] = 0.01; 1,420.7 ± 272.1 vs. 1,209.0 ± 248.5, [Formula: see text] = 0.01; 1,438.0 ± 307.2 vs. 1,213.6 ± 231.0, [Formula: see text] = 0.01). Only one reader could differentiate a difference in “intense” and “non-intense” lesions with respect to ADC[Formula: see text] (899.2 ± 248.3 vs. 711.0 ± 222.6, [Formula: see text] = 0.01) and nADC[Formula: see text] (4.4 ± 2.1 vs. 3.4 ± 1.4, [Formula: see text] = 0.05). Inter-reader agreements were slight to moderate (kappa = 0.07–0.45). Reliability substantially improved when only the lowest and highest 25th percentiles of ADC values were included (kappa = 0.17–0.75). Regression analyses showed that the “intense” lesions were associated with higher ADC values after adjustment for confounders. Conclusion: Reading of STIR MRI is limited by the lack of ability in differentiating subtle differences of spinal inflammation. ADC could be an alternative method.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.063
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.363
Teacher spread0.314 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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