Psoriasis Characteristics for the Early Detection of Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Delays in the diagnosis and treatment of psoriatic arthritis (PsA) are common. These delays contribute to impairments in quality of life and joint damage. This study aims to calculate the incidence rate of PsA over time and identify clinical features that may be used for PsA prediction in patients with psoriasis (PsO). METHODS: The study population for PsA incidence analysis included 1128 participants enrolled in the Utah Psoriasis Initiative between 2002 and 2014. Clinical evaluation and medical record review were performed to identify new cases of PsA after enrollment. To identify PsO features associated with PsA, the population was restricted to 627 participants who did not have PsA before PsO phenotyping and had been followed up for subsequent PsA diagnosis. We conducted Cox proportional hazard regressions to estimate the HR of PsA associated with PsO characteristics and other health-related features. RESULTS: < 0.001, HR 1.41). CONCLUSION: Risk of PsA increases steadily for > 60 years following PsO onset. Patient-reported history of PsO characteristics has greater predictive power than physician-measured features at enrollment visits. The characteristics identified in this study provide guidance for screening for PsA risk in patients with PsO.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".