Plasma Fibrinogen Along with Patient-reported Outcome Measures Enhances Management of Polymyalgia Rheumatica: A Prospective Study
Bibliographic record
Abstract
OBJECTIVE: We sought to prospectively examine the responsiveness of a number of patient-reported outcome (PRO) measures in polymyalgia rheumatica (PMR), as well as their relationship to the biomarkers erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and plasma fibrinogen. METHODS: Sixty patients with PMR were divided into active (n = 25) or inactive (n = 35) disease groups based on symptoms; physician assessment; and the biomarkers ESR, CRP, and plasma fibrinogen. Groups underwent assessment at baseline and 6 weeks. Disease activity measures and relevant PRO measures were recorded. Measures of responsiveness were compared for all PRO and biomarkers. RESULTS: Visual analog scale disease activity (VASDA) and VAS quality of life (VASQOL) are more responsive to change in disease activity than VAS pain, morning stiffness, Health Assessment Questionnaire (HAQ), and PMR-activity score (AS). Analysis of PMR-AS versus VASDA, VASQOL, and HAQ showed correlation coefficients of 0.87 (p < 0.001), 0.80 (p < 0.001), and 0.68 (p < 0.001), respectively. Receiver-operating curve (ROC) analysis revealed VASDA to be more specific than either HAQ (0.95 vs 0.85; p < 0.001) or VASQOL (0.95 vs 0.93; p < 0.001) for the detection of response to treatment in active PMR. Overall, fibrinogen showed superior correlation coefficients with the various PRO than either of the standard biomarkers ESR or CRP. In addition, standardized response means for fibrinogen, ESR, and CRP were 1.63, 1.2, and 1.05, respectively, indicating that plasma fibrinogen was the most responsive biomarker for assessment of change in disease activity. CONCLUSION: VASDA and VASQOL are the most responsive PRO to changes in disease activity in PMR. In addition, plasma fibrinogen demonstrated greater responsiveness to changes in disease activity and superior correlation with the various PRO measures recorded than did the standard biomarkers ESR and CRP.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".