PP36 Early Diagnosis And Treatment Of Psoriatic Arthritis
Bibliographic record
Abstract
Introduction: Screening for psoriatic arthritis (PsA) is expected to identify patients at earlier stages of the disease. Early treatment is expected to slow disease progression and delay the need for biologic therapy. This study estimated the cost-effectiveness of screening tools for PsA in Canada. Methods: A Markov model was built to estimate the associated costs and quality-adjusted life-years (QALYs) of screening tools for PsA in patients using topical treatment for psoriasis. The screening tools included: the Toronto Psoriatic Arthritis Screening (ToPAS) questionnaire; the Psoriasis Epidemiology Screening Tool (PEST); the Psoriatic Arthritis Screening and Evaluation (PASE) questionnaire; and the Early ARthritis for Psoriatic patients (EARP) questionnaire. Health states were defined by disability levels, as measured by the Health Assessment Questionnaire (HAQ), and state transition was modeled according to annual disease progression. Screening was assumed to be effective during a 2-year sojourn period. Incremental cost-effectiveness ratios (ICERs) were estimated based on health-state specific costs and utilities. A probabilistic analysis was undertaken to account for parameter uncertainty. All results were compared with the commonly cited cost-effectiveness threshold of CAD 50,000 (USD 37, 600) per additional QALY. Results: Screening with the ToPAS questionnaire resulted in cost savings compared with no screening or the EARP questionnaire, with a total cost of CAD 30,706 (USD 23,090) and 17.29 QALYs. The PEST dominated the PASE questionnaire and was more costly and more effective than the ToPAS questionnaire, with an ICER of CAD 312,398 (USD 234,909). The results were most sensitive to test sensitivity and specificity, HAQ progression, and average HAQ score at diagnosis and the start of biologic therapy. A scenario analysis tested screening efficacy for a 1-year period before diagnosis, with the ToPAS questionnaire remaining the most cost-effective option. Conclusions: Screening was cost-effective compared with no screening at the commonly used cost-effectiveness threshold of CAD 50,000 (USD 37, 600). Value of information analyses will be useful for determining the need to collect further information around test accuracy parameters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".