Diffusion-weighted imaging versus short tau inversion recovery sequence: Usefulness in detection of active sacroiliitis and early diagnosis of axial spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: To compare the utility of Diffusion weighted imaging (DWI) with short tau inversion recovery (STIR) sequence in the diagnosis of early axial spondyloarthritis (SpA). METHODS: Three hundred and five patients with chronic back pain were recruited consecutively from 3 rheumatology centers. Clinical, radiological and blood parameters were recorded. Patients with back pain duration no more than 3 years were classified as having early disease. STIR sequence and DWI of the sacroiliac joints were obtained and assessed using the Spondyloarthritis Research Consortium of Canada (SPARCC) method. The Assessment in Spondyloarthritis international Society definition was used to define positive STIR and DWI. Results were compared to expert diagnosed axial SpA. RESULTS: When compared to STIR sequence, DWI had similar sensitivity (STIR 0.29, DWI 0.30) and specificity (STIR 0.97, DWI 0.92) in diagnosing sacroiliitis. However, STIR sequence had better reliability (STIR 0.78, DWI 0.61). In early disease group, DWI was not better than STIR sequence in detecting active sacroiliitis (sensitivity DWI vs STIR: 0.34 vs 0.36; specificity DWI vs STIR: 0.93 vs 0.93; positive predictive value DWI vs STIR: 0.92 vs 0.92; negative predictive value DWI vs STIR: 0.36 vs 0.37). Using the Assessment in SpondyloArthritis international Society (ASAS) classification criteria, 67/98 patients with early disease (sensitivity 0.91 specificity 0.90) and 221/305 overall (sensitivity 0.90; specificity 0.92) were classified as axial SpA. Among the expert diagnosed axial SpA patients who did not meet the ASAS criteria, only 2 had positive DWI. CONCLUSION: DWI and STIR have similar sensitivity in diagnosing axSpA in early disease. However, the use of DWI is limited by poorer reliability when compared with STIR.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".