Polymyalgia rheumatica and giant cell arteritis: diagnosis and management
Bibliographic record
Abstract
PURPOSE OF REVIEW: There have been advances in the diagnosis and treatment of giant cell arteritis (GCA) and polymyalgia rheumatica (PMR). RECENT FINDINGS: Themes in PMR and GCA include classification criteria, ultrasound imaging of temporal and axillary arteries replacing biopsies for diagnosis of GCA, faster diagnosis and treatment with rapid access clinics for suspected GCA, and expanding treatment options with the goal of rapid suppression of inflammation and sparing steroids. SUMMARY: Treatment is aimed at suppressing inflammation quickly in both GCA and PMR. Randomized trials have demonstrated success in reducing glucocorticoids when adding advanced therapies such as interleukin 6 (IL6) inhibitors. Other treatments including Janus kinase (JAK) inhibitors (especially a phase 3 trial of upadacitinib at 15 mg daily and secukinumab (an IL17 inhibitor) are being tested. Some uncontrolled GCA protocols are limiting glucocorticoids to initial IV pulse therapy only or rapid tapering of oral glucocorticoids with upfront treatment with tocilizumab. There is uncertainty of who should have an advanced therapy and how long to use it for and what order to consider advanced therapies when treatment fails. In PMR, studies are performed when patients cannot taper glucocorticoids effectively, whereas in GCA, advanced therapies are started with disease onset or with recurrent GCA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".