Value of Color Doppler Ultrasound Assessment of Sacroiliac Joints in Patients with Inflammatory Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic value of color Doppler ultrasound (CDUS) for the detection of sacroiliitis, in patients with inflammatory back pain (IBP). METHODS: Consecutive patients with IBP and suspected axial spondyloarthritis (SpA), but without a definitive diagnosis, were included. Consecutive patients with defined SpA and axial involvement were included as a control group. All patients underwent clinical evaluation, magnetic resonance imaging (MRI), and CDUS of sacroiliac joints (SIJ) within the same week. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the diagnosis of sacroiliitis by CDUS were calculated, using MRI as the gold standard. RESULTS: There were 198 SIJ evaluated in 99 patients (36 with previous SpA). There were 61 men (61.6%), with a mean age of 39.8 years (SD 11.3) and median disease duration of 24 months (IQR 12-84). At the patient level, CDUS had a sensitivity of 63% (95% CI 48.7-75.7%) and a specificity of 89% (95% CI 76-96%). The PPV was 87.2% (95% CI 72.6-95.7%) and the NPV was 66.7% (95% CI 53.3-78.3%). At joint level, CDUS had a sensitivity of 60% (95% CI 49-70%) and a specificity of 93% (95% CI 88-98%). The PPV was 83% (95% CI 78-95%) and the NPV was 43% (95% CI 33-56%). The sensitivity of CDUS for the diagnosis of axial SpA was 54% (95% CI 36.6-71.2%), specificity was 82% (95% CI 63.1-93.9%), PPV was 79% (95% CI 57.8-92.9%), and NPV was 59% (95% CI 42.1-74.4%). CONCLUSION: CDUS showed adequate diagnostic properties for detection of sacroiliitis and is a useful tool in patients with IBP.
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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 | 0.001 | 0.011 |
| 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 | 0.001 | 0.000 |
| 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".