Impact of diabetes, angiotensin‐converting enzyme inhibitor or angiotensin II receptor blocker use, and statin use on presentation and outcomes in patients with giant cell arteritis
Bibliographic record
Abstract
AIM: Few, separate, small retrospective studies in giant cell arteritis (GCA) reported that patients: (a) with diabetes mellitus had less positive temporal artery biopsies (TAB); (b) on angiotensin-converting enzyme inhibitors (ACE-I) or angiotensin II receptor blockers (ARB) experienced fewer relapses; and (c) on statins experienced the same frequency of clinical complications and relapses as non-exposed patients. This retrospective chart review study simultaneously investigated the impact of these 3 factors on a cohort of patients followed in 2 large Canadian centers (Hamilton and Toronto, ON). METHODS: One hundred and thirty-seven patients diagnosed with GCA between 1993 and 2015 were included in the study. Presenting symptoms, TAB results, disease complications and outcomes (relapses, duration of glucocorticoid use) were compared between exposed (diabetes/ACE/ARB/statin) and non-exposed patients, with adjustment for main potential confounding variables. RESULTS: Temporal artery biopsies was less often positive in patients with pre-existing diabetes (relative risk 0.24; 95% CI: 0.069-0.81). Patients who developed diabetes after diagnosis had a lower relapse-free survival (adjusted hazards ratio [HR] 0.28; 95% CI: 0.095-0.84). Patients taking ARBs prior to diagnosis were more likely to successfully discontinue glucocorticoids without a flare in the following 3 months (adjusted HR 2.46; 95% CI: 1.2-5.3). Clinical complications and relapse rates did not differ between patients on statin therapy or not. CONCLUSION: Diabetic patients with GCA were less likely to have a positive TAB, and more likely to relapse. ARB therapy prior to diagnosis showed an association with success at discontinuing glucocorticoids. Statin therapy did not alter the clinical presentation or course of GCA.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".