Prevalence of Inflammatory Back Pain and Axial Spondyloarthritis Among University Employees in Izmir, Turkey
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of inflammatory back pain (IBP) and axial spondyloarthritis (axSpA) using the Assessment of SpondyloArthritis International Society (ASAS) classification criteria among employees in a university. METHODS: In the first stage of the study, a face-to-face interview was done using a standard questionnaire to investigate IBP in 381 subjects randomly selected from 2894 employees at Dokuz Eylul University in Izmir, Turkey. In the second stage, subjects with back pain for ≥ 3 months and age at onset < 45 years were evaluated for axSpA using the ASAS criteria. Both the European Spondyloarthropathy Study Group (ESSG) criteria and Amor criteria were used for the classification of the whole group of spondyloarthritis (SpA). RESULTS: There were 131 male and 250 female subjects (mean age: 38.0 yrs). Twenty-five subjects (6.6%) were classified as having IBP according to the ASAS criteria. The prevalence of IBP according to the Berlin and Calin criteria was 7.1% and 21.5%, respectively. The prevalence of axSpA was estimated at 1.3% according to the ASAS classification criteria (0.5% for radiographic axSpA and 0.8% for nonradiographic axSpA). A total of 7 patients (1.8%) fulfilled both the Amor and ESSG criteria for the whole group of SpA. CONCLUSION: This is the first prevalence study of IBP and axSpA using ASAS classification criteria in the Turkish population. The prevalence estimates of IBP and axSpA reported here are within the upper range of other studies in European countries and the United States.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".