Malignancies in Giant Cell Arteritis: A Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the risk of cancer in patients with biopsy-proven giant cell arteritis (GCA) from a defined population in southern Sweden. METHODS: The study cohort consisted of 830 patients (mean age at GCA diagnosis was 75.3 yrs, 74% women) diagnosed with biopsy-proven GCA between 1997 and 2010. Temporal artery biopsy results were retrieved from a regional database and reviewed to ascertain GCA diagnosis. The cohort was linked to the Swedish Cancer Registry. The patients were followed from GCA diagnosis until death or December 31, 2013. Incident malignancies registered after GCA diagnosis were studied. Based on data on the first malignancy in each organ system, age- and sex-standardized incidence ratios (SIR) with 95% CI were calculated compared to the background population. RESULTS: One hundred seven patients (13%) were diagnosed with a total of 118 new malignancies after the onset of GCA. The overall risk for cancer after the GCA diagnosis was not increased (SIR 0.98, 95% CI 0.81-1.17). However, there was an increased risk for myeloid leukemia (2.31, 95% CI 1.06-4.39) and a reduced risk for breast cancer (0.33, 95% CI 0.12-0.72) and upper gastrointestinal tract cancer (0.16, 95% 0.004-0.91). Rates of other site-specific cancers were not different from expected. CONCLUSION: In this Swedish population-based cohort of GCA, the overall risk for cancer was not increased compared to the background population. However, there was an increased risk for leukemia and a decreased risk for breast and upper gastrointestinal tract cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".