Preliminary Validation of a Self-reported Screening Questionnaire for Inflammatory Back Pain
Bibliographic record
Abstract
OBJECTIVE: Inflammatory back pain (IBP) is an important feature of axial spondyloarthritis (SpA) that is poorly recognized in primary care, perhaps delaying diagnosis of SpA. We aimed to develop and validate a self-report questionnaire using important domains reported by patients with IBP. METHODS: We developed a 6-item questionnaire assessing spinal/hip stiffness, nocturnal pain, diurnal variation, effects of exercise/rest, and peripheral joint pain/swelling. This was compared with the Calin questionnaire and the domains comprising the Assessment of Spondyloarthritis International Society (ASAS) criteria for IBP in 220 patients with established axial SpA and 66 patients with mechanical back pain followed in tertiary care rheumatology clinics. The classification utility of each item was evaluated using sensitivity, specificity, and likelihood ratio (LR). Multivariable logistic regression was used to analyze different combinations of items to develop candidate scoring systems. RESULTS: The single item "diurnal variation" had the highest combination of sensitivity (49%) and specificity (92%) for IBP (positive LR 5.95, 95% CI 2.54-13.94), outperforming the Calin and ASAS IBP criteria, which had sensitivities of 83% and 59%, specificities 42% and 66%, positive LR 1.42 and 1.72, negative LR 0.41 and 0.62, respectively. Classification utility of this item was even higher in SpA patients with disease duration < 6 years (sensitivity 48%, specificity 96%, positive LR 12, negative LR 0.54). The other 5 items did not improve classification utility in any combination. CONCLUSION: Assessment of a single self-reported item, "diurnal variation," had substantial classification utility for IBP. This domain is not addressed in existing criteria for IBP, indicating a potentially important omission.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".