Derivation of a Multivariable Psoriatic Arthritis Risk Estimation Tool (<scp>PRESTO</scp>): A Step Towards Prevention
Bibliographic record
Abstract
OBJECTIVE: A simple, scalable tool that identifies psoriasis patients at high risk for developing psoriatic arthritis (PsA) could improve early diagnosis. We aimed to develop a risk prediction model for the development of PsA and to assess its performance among patients with psoriasis. METHODS: We analyzed data from a prospective cohort of psoriasis patients without PsA at enrollment. Participants were assessed annually by a rheumatologist for the development of PsA. Information about their demographics, psoriasis characteristics, comorbidities, medications, and musculoskeletal symptoms was used to develop prediction models for PsA. Penalized binary regression models were used for variable selection while adjusting for psoriasis duration. Risks of developing PsA over 1- and 5-year time periods were estimated. Model performance was assessed by the area under the curve (AUC) and calibration plots. RESULTS: Among 635 psoriasis patients, 51 and 71 developed PsA during the 1-year and 5-year follow-up periods, respectively. The risk of developing PsA within 1 year was associated with younger age, male sex, family history of psoriasis, back stiffness, nail pitting, joint stiffness, use of biologic medications, patient global health, and pain severity (AUC 72.3). The risk of developing PsA within 5 years was associated with morning stiffness, psoriatic nail lesion, psoriasis severity, fatigue, pain, and use of systemic nonbiologic medication or phototherapy (AUC 74.9). Calibration plots showed reasonable agreement between predicted and observed probabilities. CONCLUSIONS: The development of PsA within clinically meaningful time frames can be predicted with reasonable accuracy for psoriasis patients using readily available clinical variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".