Whole‐lesion ADC histogram analysis and the spondyloarthritis research consortium of canada (SPARCC) MRI index in evaluating the disease activity of ankylosing spondylitis
Bibliographic record
Abstract
Background Conventional MRI is limited in quantitative evaluation of ankylosing spondylitis (AS) activity states. A comparison of the effectiveness of the whole‐lesion apparent diffusion coefficient (ADC) histogram analysis with the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI index in evaluating the disease activity of AS might aid in this assessment. Purpose To compare the effectiveness of the whole‐lesion ADC histogram analysis with the SPARCC MRI index in evaluating the disease activity states of AS. Study Type Prospective. Population A total of 57 AS patients and 27 healthy matched volunteers were included. Field Strength/Sequence 3.0T MR including a diffusion‐weighted imaging (DWI) sequence (b = 0, 1000 s/mm2). Statistical Tests One‐way analysis of variance (ANOVA) and Scheffe's post‐hoc was used to compare the parameters among different groups. A receiver operating characteristic (ROC) analysis and the Spearman rank correlation were performed to test the diagnostic performance of all parameters in distinguishing different disease activity states and determining the correlations between them. Assessment AS disease activity states was evaluated according to the Ankylosing Spondylitis Disease Activity Score (ASDAS). Initial DWI images and corresponding ADC maps were imported into our in‐house software. Regions of interest (ROIs) were drawn in all slices and the relevant parameters were derived simultaneously. The SPARCC MRI index scores were counted artificially based on T2‐PDW‐SPAIR images. Results The ADCmean, ADC percentiles, and SPARCC MRI index of the active group were significantly higher than the inactive and control groups (all P < 0.001). The 90th percentile could differentiate the inactive from the control group and the low disease activity group from the inactive group (P = 0.011 and 0.006, respectively). The 50th percentile of the high disease activity group was significantly higher than the low group (P = 0.004), while the SPARCC MRI index of the very high disease activity group was higher than the high group (P < 0.001). Data Conclusion The whole‐volume ADC histogram analysis was superior to the SPARCC MRI index in assessing AS activity states. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:114–126.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".