Lower Frequency of Comorbidities Prior to Onset of Giant Cell Arteritis: A Population-Based Study
Bibliographic record
Abstract
Objective To assess the frequency of comorbidities and metabolic risk factors at and prior to giant cell arteritis (GCA) diagnosis. Methods This is a retrospective case control study of patients with incident GCA between January 1, 2000, and December 31, 2019, in Olmsted County, Minnesota. Two age- and sex-matched controls were identified, and each assigned an index date corresponding to an incidence date of GCA. Medical records were manually abstracted for comorbidities and laboratory data at incidence date, 5 years, and 10 years prior to incidence date. Twenty-five chronic conditions using International Classification of Diseases, 9th revision, diagnosis codes were also studied at incidence date and 5 years prior to incidence date. Results One hundred and twenty-nine patients with GCA (74% female) and 253 controls were identified. At incidence date, the prevalence of diabetes mellitus (DM) was lower among patients with GCA (5% vs 17%;P= 0.001). At 5 years prior to incidence date, patients were less likely to have DM (2% vs 13%;P< 0.001) and hypertension (27% vs 45%;P= 0.002) and had a lower mean number (SD) of comorbidities (0.7 [1.0] vs 1.3 [1.4];P< 0.001) compared to controls. Moreover, patients had significantly lower median fasting blood glucose (FBG; 96 mg/dL vs 104 mg/dL;P< 0.001) and BMI (25.8 vs 27.7;P= 0.02) compared to controls. Multivariable logistic regression analysis revealed negative associations for FBG with GCA at 5 and 10 years prior to diagnosis/index date. Conclusion DM prevalence and median FBG and BMI were lower in patients with GCA up to 5 years prior to diagnosis, suggesting that metabolic factors influence the risk 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".