Risk of Cancer in 767 Patients with Giant Cell Arteritis in Western Norway: A Retrospective Cohort with Matched Controls
Bibliographic record
Abstract
OBJECTIVE: To determine the risk of cancer in a large Norwegian cohort of patients with giant cell arteritis (GCA). METHODS: This is a hospital-based, retrospective, observational cohort study including patients diagnosed with GCA in the Bergen Health Area during 1972-2012. Patients were identified through computerized hospital records using the International Classification of Diseases coding system. Medical records were reviewed. Each patient was randomly assigned population controls matched on age, sex, and geography from the Central Population Registry of Norway. Data on the occurrence of cancer were obtained from the Cancer Registry of Norway. The cumulative risk of malignancy was estimated using Kaplan-Meier methods and potential differences were analyzed using the Gehan-Breslow and log-rank tests. RESULTS: We identified 881 cases with a clinical diagnosis of GCA, of which 792 fulfilled the American College of Rheumatology (ACR) 1990 classification criteria and 528 were biopsy-verified. Cases with no registered cancer prior to GCA diagnosis were included in a time-to-event analysis, with first cancer as the event (n = 767 with clinical GCA diagnosis, 686 fulfilling ACR criteria for GCA, 463 biopsy-verified). These cases were matched with previously cancer-free population controls (n = 1437, 1284, 895, respectively). We found no significant difference in the risk of malignancy after time of diagnosis/matching for GCA patients compared to population controls (p > 0.05). CONCLUSION: In this study of a large and well-characterized cohort of patients with GCA, there was no difference in the risk of malignancy in patients with GCA compared to matched population controls.
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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 | 0.001 |
| 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 | 0.000 |
| 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".