Psoriatic Arthritis Screening: A Systematic Review, Meta-Analysis, and Economic Evaluation
Bibliographic record
Abstract
Psoriatic arthritis (PsA) is an autoimmune disease that affects the skin and the musculoskeletal system. It causes joint damage and psoriasis of the skin. Untreated disease is usually related to a delayed diagnosis and has been associated with physical disability and high treatment costs later on. Although expensive biologic therapy has proven to slow disease progression and improve health outcomes, rheumatologists have suggested initiating treatment with less expensive Disease Modifying Anti-Rheumatic Drugs (DMARDs). Identifying early PsA is expected to improve health outcomes through early treatment with DMARDs. It is also expected to reduce the proportion of severe disease and biologic treatment. Given that the prevalence of PsA among psoriasis patients is relatively high, dermatologists are well-positioned to screen for arthritis symptoms with already validated self-administered screening questionnaires for patients with psoriasis. The goal of this thesis is to systematically review the characteristics and accuracy estimates of the validated PsA screening tools (chapter 2). It also seeks to evaluate the cost-effectiveness of implementing a PsA screening program in Canada relative to the current practice where psoriasis patients are not systematically screened (chapter 3). The National Institute of Health Research is currently developing a randomized controlled trial for PsA screening in the United Kingdom that will inform the cost-effectiveness model presented in this thesis.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".