What influences patients’ opinion of remission and low disease activity in psoriatic arthritis? Principal component analysis of an international study
Bibliographic record
Abstract
OBJECTIVE: In PsA, the treatment objective is remission or low disease activity (LDA), but patients' perception of remission is poorly studied. This analysis aimed to identify factors associated with patient-defined remission. METHODS: This analysis uses ReFlaP data, an international PsA study, with remission defined as 'At this time, is your psoriatic arthritis in remission, if this means: you feel your disease is as good as gone?'. Variables associated with, first, patient-defined remission and, second, LDA were identified using multivariable logistic regression and principal component analysis (PCA) to explore correlated variables. RESULTS: Of 424 patients (50.2% male, mean age 52 years) with established disease, 94 (22.2%) reported themselves as being in remission and 191 (45.0%) as LDA alone. In multivariable analysis pain, psoriasis, impact of disease, physician opinion of symptoms from joint damage and Groll comorbidity index were independent predictors of remission. For LDA, results were similar. Using PCA, variance explained was 74% by five components for men and 80% by six components for women. The key component from PCA for remission was, for both sex, disease impact (Psoriatic Arthritis Impact of Disease, pain and HAQ) explaining 22.2-27.5% of variance. Other factors included musculoskeletal disease activity, chronicity/joint damage, psoriasis, enthesitis and CRP. For LDA, similar factors were identified but the variance explained was lower (64-68%). CONCLUSION: Many factors impact on patients' opinion of remission, dominated by disease impact. Disease activity in multiple domains, chronicity/age, comorbidities and symptoms due to other conditions contribute to a robust model highlighting that patient-defined remission is multifaceted. TRIALS REGISTRATION: Clinicaltrials.gov, http://clinicaltrials.gov, NCT03119805.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".