Incidence and prevalence of axial spondyloarthritis: methodologic challenges and gaps in the literature.
Bibliographic record
Abstract
OBJECTIVES: The incidence and prevalence of axial spondyloarthritis (axSpA), including ankylosing spondylitis (AS) and non-radiographic (nr-)axSpA, have been investigated in multiple populations, though there is a paucity of population-level data. Here, we identify population-based studies in AS and nr-axSpA, and describe the methodologic challenges in conducting these, outlining potential reasons for disparate incidence and prevalence estimates. METHODS: PubMed and Embase were searched for population-based studies providing incidence and prevalence rates, published in English from 1 Jan 2000-30 Jun 2015. Extracted information included incidence/prevalence rates, geographical population, study design, data source, case definition, age/gender, and classification criteria used. RESULTS: Of 2,148 articles identified, 19, from 15 countries, fulfilled eligibility criteria. Incidence rates per 100,000 patient-years were reported in 4 AS studies and varied from 0.4 (Iceland) to 15.0 (Canada). Reported AS prevalence rates per 100,000 persons also showed considerable variation (16 studies: 6.5 [Japan] to 540.0 [Turkey]). Only 3 axSpA and no nr-axSpA prevalence rates were reported. Considerable variation was seen in the methodology used to estimate incidence and prevalence rates, e.g. screening method, study design, and classification criteria. Although the prevalence of AS is known to vary by HLA-B27 status, only 4 studies reported this genetic marker. CONCLUSIONS: There is an unmet need for future studies to use consistent methodology, capture all relevant information (including HLA-B27 positivity), and investigate under-reported populations (e.g. nr-axSpA; southern hemisphere countries) to estimate the population burden of axSpA. Future studies should aim to address data gaps to provide accurate incidence/prevalence estimates for the global axSpA population.
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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 | 0.163 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".