Using Acute-phase Reactants to Inform the Development of Instruments for the Updated Psoriatic Arthritis Core Outcome Measurement Set
Bibliographic record
Abstract
OBJECTIVE: Systemic inflammationˆ is assessed through measurement of acute-phase reactants such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). With few exceptions, most randomized controlled trials (RCT) have assessed acute-phase reactants (CRP and ESR) as part of the American College of Rheumatology (ACR) 20 response criteria. As part of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Outcome Measures in Rheumatology (OMERACT) working group, we performed a systematic review of the literature to assess the performance of inflammatory biomarkers in psoriatic arthritis (PsA). METHODS: A systematic search of PubMed and Embase was performed. The search included peer-reviewed articles and scientific meeting abstracts about RCT and longitudinal observational studies that assessed systemic inflammation using acute-phase reactants in PsA. Studies were assessed following the components of the OMERACT filter including construct validity, responsiveness, and predictive validity. RESULTS: There were 2764 articles retrieved, and 71 articles were included for this systematic review. Twenty-eight articles reported CRP and/or ESR separately, and the remaining articles reported CRP and/or ESR as part of the ACR response criteria. Studies assessing OMERACT responsiveness provided conflicting reports. Inflammatory biomarkers had construct validity for more active disease. Evidence suggests that an elevation of ESR predicts cardiovascular outcomes. CONCLUSION: Data regarding assessment of systemic inflammation using acute-phase reactants (CRP and ESR) are limited. There is only weak evidence to support normalization of these biomarkers in predicting good clinical outcomes/remission criteria. The predictive value for cardiovascular outcomes was generally good. Further studies to assess systemic inflammation in PsA using acute-phase reactants and other laboratory biomarkers are needed.
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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 | 0.085 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".