Factors Associated with Relapse and Dependence on Glucocorticoids in Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: To identify characteristics and factors associated with relapse and glucocorticoid (GC) dependence in patients with giant cell arteritis (GCA). METHODS: We retrospectively analyzed 326 consecutive patients with GCA followed for at least 12 months. Factors associated with relapse and GC dependence were identified in multivariable analyses. RESULTS: The 326 patients (73% women) were followed up for 62 (12-262) months. During followup, 171 (52%) patients relapsed, including 113 (35%) who developed GC dependence. Relapsing patients had less history of stroke (p = 0.01) and presented large-vessel vasculitis (LVV) more frequently on imaging (p = 0.01) than patients without relapse. During the first months, therapeutic strategy did not differ among relapsing and nonrelapsing patients. GC-dependent patients were less likely to have a history of stroke (p = 0.004) and presented LVV on imaging more frequently (p = 0.005) than patients without GC-dependent disease. In multivariable analyses, LVV was an independent predictive factor of relapse (HR 1.49, 95% CI 1.002-2.12; p = 0.04) and GC dependence (OR 2.19, 95% CI 1.19-4.05; p = 0.01). Conversely, stroke was a protective factor against relapse (HR 0.21, 95% CI 0.03-0.68; p = 0.005) and GC-dependent disease (OR 0.10, 95% CI 0.001-0.31; p = 0.0005). Patients with a GC-dependent disease who received a GC-sparing agent had a shorter GC treatment duration than those without (p = 0.008). CONCLUSION: In this study, LVV was an independent predictor of relapse and GC dependence. Further prospective studies are needed to confirm these findings and to determine whether patients with LVV require a different treatment approach.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".