Contribution of the Polymyalgia Rheumatica Activity Score to Glucocorticoid Dosage Adjustment in Everyday Practice
Bibliographic record
Abstract
OBJECTIVE: To evaluate the usefulness of the polymyalgia rheumatica (PMR) activity score (PMR-AS) in guiding adjustment of glucocorticoid (GC) dosage. METHODS: Rheumatologists prospectively included patients receiving GC therapy for PMR. At each visit, they assessed disease activity using a visual analog scale for physician's global assessment (VASph) and recorded whether a flare was diagnosed and/or the GC dosage was changed. In each patient, the PMR-AS was calculated using the formula of Leeb and Bird: C-reactive protein (mg/dl) + VAS pain score (0 to 10) + VASph (0 to 10) + (morning stiffness in min × 0.1) + elevation of upper limbs (0-3). We evaluated the correlation between PMR-AS and GC dosage changes in the group already treated with GC. RESULTS: We included 89 patients (mean age 74.6 ± 6.2 yrs; disease duration 1.6 ± 2.2 yrs), who had a total of 149 visits. PMR-AS was available for 137 visits. Of those, 124 involved patients already treated with GC, and 13 patients who started GC treatment. The Spearman correlation coefficient between PMR-AS values and GC dosage change was 0.58 (p < 0.001). In the group already treated with GC, when the PMR-AS was higher than 20, GC dosages were never decreased. When the PMR-AS was between 10 and 20, GC dosages were decreased in 4 patients, unchanged in 4, and increased by < 5 mg in 4 patients. When PMR-AS was < 10, GC dosages were generally decreased. CONCLUSION: The PMR-AS is helpful for diagnosing flares of PMR and may also assist in everyday practice to decide how to change the GC dosage.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".