Symptom Severity and Glucocorticoid Dosing in Patients With Polymyalgia Rheumatica and Obesity
Bibliographic record
Abstract
Objective Polymyalgia rheumatica (PMR) is an inflammatory disorder of the elderly characterized by girdle pain and stiffness. Obesity has an influence on disease activity and outcome in rheumatic diseases like osteoarthritis and rheumatoid arthritis. We aimed to investigate the relationship between high BMI and the severity and outcome of PMR, which is incompletely understood. Methods In a post hoc analysis, 83 patients with recent-onset PMR were studied over 6 months using clinical examination, laboratory evaluation, and girdle ultrasound (US). The modified Health Assessment Questionnaire (mHAQ), 36-item Short Form Health Survey (SF-36), and PMR visual analog scale (VAS) scores, as well as prednisone therapy data, were recorded. Patients were grouped according to their BMI. Results At baseline, the 12 patients with obesity had significantly more shoulder pain (P= 0.03), global pain (P= 0.03), PMR VAS (P< 0.01), and fatigue (P= 0.03); higher mHAQ (P= 0.01); and lower SF-36 physical component summary (P= 0.048) and SF-36 pain index (P< 0.001). The mean initial prednisone dose was similar among groups, but patients with obesity received a lower dose/kg (1.9 [SD 0.7] mg vs 2.2 [SD 0.7] mg;P< 0.01). At 6 months, patients with obesity were being treated with higher mean daily prednisone doses (8.5 [SD 3.2] mg/d vs 6.2 [SD 5.2] mg/d;P= 0.02), and 40% of them were receiving higher daily prednisone doses than the standard protocol compared with 14% patients without obesity (P= 0.048). Clinical features, laboratory results, and US results were similar between patients with and without obesity. Conclusion Obesity affects both symptom severity and prednisone utilization in patients with PMR. The reason for this may relate to different subjective pain perception rather than increased inflammation in patients with obesity. BMI should be considered when interpreting symptoms in patients with PMR and deciding their prednisone doses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".