Assessing the construct validity of clinical tests to identify sacroiliac joint inflammation in patients with non‐radiographic axial spondyloarthritis
Bibliographic record
Abstract
AIM: Magnetic resonance imaging (MRI) can be used to identify sacroiliac joint (SIJ) inflammation and provide an earlier diagnosis of nonradiographic axial spondyloarthritis (nrAxSpA). However, MRI is frequently a resource-limited examination. Our aim was to assess if a set of physical clinical tests can identify SIJ inflammation in patients with nrAxSpA. METHODS: Twenty participants with nrAxSpA underwent two functional tests (active straight leg raise, and stork test on the support side) and four pain provocation tests (Gaenslen's, posterior pelvic pain provocation, Patrick's Faber and palpation of the long dorsal SIJ ligament) for the SIJ, and then proceeded to a contemporaneous reference standard MRI. The Spondyloarthritis Research Consortium of Canada scoring system (SPARCC) was used to score MRI. Specificity, sensitivity, and likelihood ratios (LR) were calculated for individual clinical tests, and for the composite of tests. RESULTS: Pain provocation tests were superior to functional tests, which showed poor accuracy. The Patrick's Faber test was the best performing procedure (sensitivity 71%, specificity 75%, positive LR 2.9, negative LR 0.4). When combining the provocation tests, a positive test in one out of two tests demonstrated the strongest predictive value (sensitivity 86%, specificity 62%, positive LR 2.2, negative LR 0.2). CONCLUSIONS: Sacroiliac joint pain provocation tests correlate modestly with inflammation. The Patrick's Faber test showed the greater LR to identify SIJ inflammation in patients with nrAxSpA. SIJ pain provocation tests may offer a simple and cost-effective way of identifying patients with nrAxSpA who are most likely to have MRI evidence of inflammation.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".