Evaluation of 2 Screening Strategies for Early Identification of Patients with Axial Spondyloarthritis in Primary Care
Bibliographic record
Abstract
OBJECTIVE: To evaluate 2 referral strategies for axial spondyloarthritis (SpA) in patients with chronic low back pain at the primary care level. METHODS: Referral physicians (n = 259) were randomly assigned to either Strategy 1 or Strategy 2 in order to refer patients with chronic back pain (duration > 3 months), age at onset of back pain < 45 years, and no diagnosis of axial SpA, to a cooperating rheumatologist (n = 43). According to Strategy 1, suitable patients were referred if at least 1 of the following screening criteria was present: inflammatory back pain, HLA-B27, or sacroiliitis detected by imaging. According to Strategy 2, patients were referred if 2 out of 5 criteria were positive: the same 3 criteria from Strategy 1 and additionally a positive family history of ankylosing spondylitis (AS) or a good treatment response to nonsteroidal antiinflammatory drugs. The final diagnosis of the rheumatologist was used as the "gold standard." RESULTS: In total, 560 consecutively referred patients were included in the analysis. Among 318 patients referred by Strategy 1, 41.8% (95% CI 36.5%-47.3%) were diagnosed with definite axial SpA. Among 242 patients referred by the second strategy, definite axial SpA was diagnosed in 36.8% (95% CI 31.0%-43.0%) of the cases. CONCLUSION: Both referral strategies demonstrated comparable performance in identification of patients with axial SpA. Strategy 1 might be preferred as an easy and reliable screening method for axial SpA at the primary care level.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".