Comparing diffusion weighted imaging with clinical and blood parameters, and with short tau inversion recovery sequence in detecting spinal and sacroiliac joint inflammation in axial spondyloarthritis.
Bibliographic record
Abstract
OBJECTIVES: To investigate the usefulness of diffusion weighted imaging (DWI) by comparing with clinical features, blood parameters and traditional short tau inversion recovery (STIR) sequence in detecting spinal and sacroiliac (SI) joint inflammation in axial spondyloarthritis (axSpA) patients. METHODS: One hundred and ten axSpA patients were recruited. Clinical, radiological and blood parameters were recorded. DWI and STIR MRI were performed simultaneously and results were scored according to the Spondyloarthritis Research Consortium of Canada (SPARCC) for comparison. Apparent diffusion coef cient (ADC) values were also calculated. RESULTS: DWI did not correlate with clinical parameters or blood parameters. It also had lowered sensitivity. When compared with STIR sequence, it correlated well with STIR sequence at the SI joint level (CC 0.76, p<0.001), but weakly at the spinal level (CC 0.23, p=0.02). At the SI joint level, the presence of inflammation on both STIR sequence and DWI was associated with an increase in maximum (B=0.24, p=0.02 in STIR; B=0.37, p<0.001 in DWI) and mean ADC values (B=0.17, p=0.003 in STIR; B=0.15, p=0.01 in DWI). Maximum (B=0.19, p=0.04) and mean spinal ADC values (B=0.18, p=0.01) were also positively associated with DWI detected spinal inflammation. Presence of Modic lesions showed positive correlation with STIR sequence (B=7.12, p=0.01) but not spinal ADC values. CONCLUSIONS: Despite DWI correlates with STIR sequence, it has lower sensitivity. However, ADC values appear to be independent of Modic lesions and may supplement STIR sequence to differentiate degeneration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".