Development of Giant Cell Arteritis after Treating Polymyalgia or Peripheral Arthritis: A Retrospective Case-control Study
Bibliographic record
Abstract
OBJECTIVE: We investigated the development of giant cell arteritis (GCA) in patients with prior diagnoses of isolated polymyalgia rheumatica and/or peripheral arthritis (PMR/PA), and the potentially relevant characteristics of both illnesses in such patients. METHODS: We retrospectively compared the features of 67 patients at the onset of PMR/PA, and their outcomes, to those of a random group of 65 patients with PMR/PA who did not develop late GCA. We also compared the features and outcomes of patients with late GCA to those of a random sample of patients with more usual GCA (65 with concurrent PMR/PA and 65 without). RESULTS: Patients with late GCA represented 7.4% of all patients with GCA included in a large hospital-based inception cohort. PMR/PA preceded overt GCA by 27 months on average. Permanent visual loss developed in 10 patients, including 8 of 48 (17%) patients featuring cranial arteritis. A questionable female predominance was the only distinguishing feature of PMR/PA evolving into GCA; late GCA more often featured subclinical aortitis (OR 6.42, 95% CI 2.39-17.23; p < 0.001), headache (OR 0.44, 95% CI 0.19-1.03; p = 0.06), and fever (OR 0.29, 95% CI 0.13-0.64; p = 0.002) less often compared to the more usual form of GCA. Patients with either form of GCA experienced similar outcomes. CONCLUSION: A cranial arteritis pattern of late GCA is associated with a significant risk for ischemic blindness. However, compared to the usual form of GCA, late GCA is often less typical, with a higher frequency of silent aortitis. Patients with relapsing/refractory PMR may not be at increased risk for late 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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".