Seasonal Variation in the Activity of Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is any seasonal variation in the activity of systemic lupus erythematosus (SLE) overall and by individual organs. METHODS: The study group comprised 2102 patients with SLE who were followed in a prospective longitudinal cohort study. In this cohort, 92.3% of the patients were women. The mean ± SD age of the patients was 47.9 ± 13.9 years, 56.3% were white, 37.1% were African American, and 3.1% were Asian. Global disease activity was recorded by the Safety of Estrogens in Lupus Erythematosus National Assessment - Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) and the physician's global assessment. Activity of each organ was also recorded using SLEDAI terms and a visual analog scale (VAS; 0 to 3). RESULTS: There was significant seasonal variation in photosensitive rash (p < 0.0001), which was more frequent in the spring and summer months (p < 0.0001). There was significantly more arthritis activity in spring and summer, as measured by both SELENA-SLEDAI (p = 0.0057) and the joint VAS (p = 0.0047). A decrease in renal activity was found in the summer months compared to the rest of the year (p = 0.0397). Serositis recorded by VAS had higher activity from August to October (p = 0.0392). Anti-dsDNA levels were significantly higher during October and November (p < 0.0001). There was significant seasonal variation in antiphospholipid antibody levels (p < 0.0001) and lupus anticoagulant (p = 0.0003). We found a significant variation in activity through the year in global disease activity as measured by SELENA-SLEDAI (p = 0.048). CONCLUSION: In the Hopkins Lupus Cohort, skin and joint activity is increased during the spring and summer, but other organs have different patterns. These seasonal variations likely reflect environmental factors that influence disease activity, including ultraviolet light and infections.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".