Model to Determine the Cost‐Effectiveness of Screening Psoriasis Patients for Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Screening psoriasis patients for psoriatic arthritis (PsA) is intended to identify patients at earlier stages of the disease. Early treatment is expected to slow disease progression and delay the need for biologic therapy. Our objective was to determine the cost-effectiveness of screening for PsA in patients with psoriasis in Canada. METHODS: A Markov model was built to estimate the costs and quality-adjusted life years (QALYs) of screening tools for PsA in psoriasis patients. The screening tools included the Toronto Psoriatic Arthritis Screen, Psoriasis Epidemiology Screening Tool, Psoriatic Arthritis Screening and Evaluation, and Early Psoriatic Arthritis Screening Questionnaire (EARP) questionnaires. States of health were defined by disability levels as measured by the Health Assessment Questionnaire. State transitions were modeled based on annual disease progression. Incremental cost-effectiveness ratios and incremental net monetary benefits were estimated. Sensitivity analyses were undertaken to account for parameter uncertainty and to test model assumptions. RESULTS: Screening was cost-effective compared to no screening. The EARP tool had the lowest total cost ($2,000 per patient per year saved compared to no screening) and the highest total QALYs (additional 0.18 per patient compared to no screening). The results were most sensitive to test accuracy and the efficacy of disease-modifying antirheumatic drugs (DMARDs). No screening was cost-effective (at $50,000 per QALY) relative to screening when DMARDs failed to slow disease progression. CONCLUSION: If early therapy with DMARDs delays biologic treatment, implementing screening in patients with psoriasis in Canada is expected to represent a cost savings of $220 million per year and improve the quality of life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".