Patient’s Global Assessment as an Outcome Measure for Psoriatic Arthritis in Clinical Practice: A Surrogate for Measuring Low Disease Activity?
Bibliographic record
Abstract
OBJECTIVE: To assess the low disease activity (LDA) in a group of patients with psoriatic arthritis (PsA) receiving antitumor necrosis factor-α (TNF-α) by using the patient's global assessment (PtGA) in clinical practice, and to compare PtGA with minimal disease activity (MDA) and other outcome measures. METHODS: Patients with PsA classified by the ClASsification for Psoriatic ARthritis (CASPAR) criteria and consecutively admitted to an outpatient clinic dedicated to biologic therapy were assessed during their routine followup. The primary outcome measure was the proportion of patients achieving a PtGA ≤ 20 at 4-, 8-, and 12-month followups. Secondary outcome measures included the proportion of patients achieving MDA and other outcome measures. Correlation of PtGA with MDA and other process and outcome measures were also performed. RESULTS: During the period of observation, 124 patients were evaluated. PtGA ≤ 20 was achieved in 25.7% at 4 months, 48.9% at 8 months, and 65.3% at 12 months of followup. The percentage of PtGA ≤ 20 statistically improved throughout the 3 timepoint assessments and it was statistically correlated to MDA. A significant correlation with the Disease Activity index for PSoriatic Arthritis (DAPSA), Bath Ankylosing Spondylitis Disease Activity Index, and Health Assessment Questionnaire was also observed. MDA, DAPSA, and Disease Activity Score at 28 joints with C-reactive protein remission were achieved at 12 months in 64%, 36%, and 71% of patients, respectively. CONCLUSION: PtGA can estimate the LDA status and could be considered as a surrogate of outcome measures for the assessment of global disease activity in patients with PsA receiving anti-TNF therapy during routine clinical practice. These data suggest that PtGA might be used in outpatient settings, being a simple, reliable, and not time-consuming instrument.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".