Temporal Trends of Venous Thromboembolism Risk Before and After Diagnosis of Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: Giant cell arteritis (GCA) and the use of glucocorticoids have both been associated with increased risk of venous thromboembolism (VTE). However, the possibility of confounding by indication has not been investigated. We undertook this study to examine the temporal risk of VTE in GCA patients before and after GCA diagnosis, accounting for confounders including glucocorticoid treatment. METHODS: We conducted a matched cohort study using an electronic medical record database representative of the UK population (1990-2013). We calculated age-, sex-, and entry time-matched and multivariate relative risks (RRs) of VTE, comparing 6,441 patients with new-onset GCA (defined by corresponding diagnosis codes and prescribed glucocorticoid treatment) to 63,985 controls before and after GCA diagnosis. Analysis before GCA diagnosis was stratified by oral glucocorticoid use to account for confounding. RESULTS: There were 27 incident VTE events during the 12 months preceding GCA diagnosis and 195 afterward. Compared to controls, during the 12, 9, 6, and 3 months preceding GCA diagnosis, the age-, sex-, and entry time-matched RRs for VTE among patients with imminent GCA not treated with glucocorticoids were 1.8, 2.2, 2.4, and 3.6, respectively. In the first 3, 6, 12, 24, 48, and 96 months after GCA diagnosis, the corresponding RRs were 9.9, 7.7, 5.9, 4.4, 3.3, and 2.4. Multivariate analyses including several common VTE risk factors showed similar trends. CONCLUSION: The risk of VTE increases shortly before GCA diagnosis, peaks at the time of diagnosis, and then progressively declines thereafter. This risk is apparent in patients with imminent GCA unexposed to oral glucocorticoids, suggesting a role for inflammation-associated thrombosis that is independent of glucocorticoid use.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".