Localized Myofascial Inflammation Revealed by Magnetic Resonance Imaging in Recent-onset Polymyalgia Rheumatica and Effect of Tocilizumab Therapy
Bibliographic record
Abstract
Objective. To assess the prevalence of myofascial inflammatory lesions visible by magnetic resonance imaging (MRI) and their changes after tocilizumab (TCZ) therapy in active polymyalgia rheumatica (PMR). Methods. We conducted a posthoc analysis of data from the TENOR study of TCZ monotherapy in PMR. The 18 patients each received TCZ injections at weeks 0, 4, and 8. The shoulder and pelvic girdles were assessed at baseline then at weeks 2 and 12 using T1- and T2- short-tau inversion recovery–weighted MRI. Radiologists blinded to patient data assessed each muscle group for localized myofascial inflammation on baseline, Week 2, and Week 12 MRI. Reproducibility was estimated by having 2 radiologists assess the Week 2 MRI of 13 patients, then computing the κ coefficient. Results. For myofascial lesion detection, intraobserver reproducibility was almost perfect (κ = 0.890) and interobserver reproducibility was substantial (κ = 0.758). At baseline, all patients had at least 1 inflammatory myofascial lesion; sites involved were the shoulder in 10 (71.4%) patients, hip in 13 (86.7%), ischial tuberosity in 9 (60.0%), and pubic symphysis in 12 (80.0%). Sites involved at Week 12 were the shoulder in 8 patients (53.3%), hip in 5 (33.3%), ischial tuberosity in 1, and pubic symphysis in 3 (20.0%). At Week 12, of 103 muscle groups studied in all, 43 (41.7%) had no inflammatory lesions, compared to 33 at baseline (p = 0.002); improvements were noted in 66 (64.1%) muscle groups, worsening in 2 (1.9%), no change in 35 (34.0%; p = 0.034). Conclusion. Localized myofascial inflammatory lesions are common in recent-onset PMR and improve during TCZ therapy. Clinicaltrials.gov ( NCT01713842 ).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".