Which Measure of Inflammation to Use? A Comparison of Erythrocyte Sedimentation Rate and C-Reactive Protein Measurements from Randomized Clinical Trials of Golimumab in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess clinical utility of measurements of C-reactive protein (CRP) versus Westergren erythrocyte sedimentation rate (ESR) in evaluating patients with rheumatoid arthritis (RA). METHODS: Data from 3 randomized clinical trials of golimumab involving 1247 patients with RA in which ESR and CRP were obtained at baseline and Week 24, along with standard measures of clinical disease activity [swollen and tender joint counts, global disease activity assessment, composite Disease Activity Scores (DAS) and Clinical Disease Activity Index (CDAI)], were utilized. Result. Both ESR and CRP were significant predictors of swollen joint count (p < 0.001 for each). Only 4.5% of patients with no swollen joints had elevated CRP and normal ESR, but 15.2% had elevated ESR and normal CRP. ESR and CRP correlated significantly (Pearson r = 0.59, p < 0.001) with each other. DAS-ESR and DAS-CRP were highly correlated (r = 0.96, p < 0.001) with each other, although DAS-ESR values were slightly lower than the DAS-CRP values at the upper end of the range (DAS > 8). Both ESR and CRP were significantly associated with CDAI (p < 0.001 for each). CONCLUSION: It is not necessary to obtain both ESR and CRP measures for clinical disease activity assessment in clinical trials of RA. Neither test adds significantly to clinical measures of disease activity including joint counts and global assessments. Where available, the CRP alone may be preferred for disease activity assessment as a simple, validated, reproducible, non age-dependent test.
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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 | 0.102 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".