Oligoarticular vs Polyarticular Psoriatic Arthritis: A Longitudinal Study Showing Similar Characteristics
Bibliographic record
Abstract
Objective The objectives of this study were to determine whether patients with oligoarticular presentation differ from those with polyarticular presentation and to identify potential predictors for evolution of oligoarthritis to polyarthritis in patients with psoriatic arthritis (PsA). Methods Patients who entered the University of Toronto PsA clinic between 1978 and 2018 within 12 months of diagnosis were identified. Only patients with ≥ 2 clinic visits were included. Patients were followed at 6- to 12-month intervals according to standard protocol, which included demographics, clinical history, detailed clinical examination, laboratory information, and patient questionnaires. Radiographs were done at 2-year intervals.Oligoarthritiswas defined by the presence of ≤ 4 inflamed joints andprogressionas an increase to ≥ 5 joints. Statistical analyses included logistic regression models as well as Weibull regression models, adjusted for age, disease duration, and sex. Results Of 407 patients, 192 (47%) presented with oligoarthritis. Whereas demographic features were similar to those with polyarthritis, more patients with polyarthritis presented with dactylitis and enthesitis. Similar joint distribution was observed, with small joints of the hands and feet being most commonly affected. Patients with polyarthritis had higher Health Assessment Questionnaire and lower 36-item Short Form Health Survey (SF-36) scores. Of the 192 oligoarticular patients, 117 (61%) remained oligoarticular and 75 (39%) progressed to polyarthritis. A lower SF-36 mental component summary (MCS) score was the predictor for progressing to polyarthritis. Conclusion Oligoarticular PsA occurs in 47% of patients with PsA and is similar to polyarticular disease, with most patients having small joint involvement. The only predictor for progression to polyarthritis was lower SF-36 MCS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".