The National Prevalence of Clinically Diagnosed Psoriatic Arthritis in Sweden in 2017
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) prevalence estimates vary across studies; studies based on national data are few. We aimed to estimate the prevalence of clinically diagnosed PsA in Sweden in 2017, overall and stratified by sex, age, education, and geography, and to quantify disease-modifying antirheumatic drug (DMARD) use among those in contact with specialized rheumatology care between 2015 and 2017. METHODS: Individuals who were 18 to 79 years of age, alive and residing in Sweden on December 31, 2017, and had a prior PsA diagnosis were identified from the National Patient Register (NPR) and/or the Swedish Rheumatology Quality Register (SRQ). PsA prevalence was estimated according to a base case (BC) definition (ie, ≥ 1 main PsA International Classification of Diseases code from rheumatology or internal medicine departments in the NPR or a PsA diagnosis in the SRQ), according to 4 sensitivity analysis definitions, and for those seen in specialized rheumatology care between 2015 and 2017. In the latter group, DMARD use during 2017 was also assessed. Data for stratifications were retrieved from national registers. RESULTS: The crude national prevalence of PsA for adults, aged 18 to 79 years, was estimated at 0.39%, according to the BC definition; 0.34% after accounting for diagnostic misclassification; and 0.32% to 0.50% across all sensitivity analyses. The prevalence was lower in males and in those with a higher level of education. The prevalence for those seen in specialized rheumatology care between 2015 and 2017 was estimated at 0.24%. During 2017, 32% of patients in this population received biologic or targeted synthetic DMARDs, and 41% received conventional synthetic DMARDs only. CONCLUSION: The prevalence of clinically diagnosed PsA in adults, aged 18 to 79 years, in Sweden in 2017 was around 0.35%. Among PsA cases in recent contact with specialized rheumatology care, almost three-fourths received DMARD therapy in 2017.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".