Clinical and Demographic Characteristics of Erosion-free and Erosion-present Status in Psoriatic Arthritis in a Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) has been recognized as a severe erosive disease. However, some patients do not develop erosions. We aimed to determine the prevalence, characteristics, and predictors of erosion-free patients (EFP) as compared with erosion-present patients (EPP) among patients with PsA followed prospectively. METHODS: This is a retrospective analysis conducted on patients from the Toronto PsA cohort. Patients with at least 10 years of followup and radiographs were analyzed. Radiographs were scored with the modified Steinbrocker method. Baseline (first visit to clinic) characteristics were used to predict the development of erosions with logistic regression models. To examine the effect of time-varying covariates, Cox regression models were fit for the time to development of erosions from baseline. RESULTS: Among 290 patients, 12.4% were EFP and 87.6% were EPP over the study period. The mean time to development of erosion in the EPP over the course of followup was 6.8 ± 6.1 years. EFP were diagnosed with psoriasis at a younger age compared with EPP. In both models, actively inflamed joints and clinically damaged joints were predictive of the development of erosion, whereas a longer duration of psoriasis at baseline decreased the odds of developing erosion. EPP had a higher percentage of unemployment as compared with EFP at baseline and followup visits. CONCLUSION: Among patients with PsA followed for at least 10 years, 12.4% never develop erosions. The clinical and radiographic findings can ultimately assist in the stratification of a patient's prognosis regarding the development of erosions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".