Optimal Frequency of Visits for Patients with Systemic Lupus Erythematosus to Measure Disease Activity Over Time
Bibliographic record
Abstract
OBJECTIVE: adjusted mean Systemic Lupus Erythematosus Disease Activity Index (SLEDAI; AMS) measures lupus disease activity over time. Our aim was to determine optimal visit frequency for calculating AMS. METHODS: patients followed monthly for 12 consecutive visits were included. AMS was calculated using all of the SLEDAI 2000 (AMS(GOLD) using all 12 visits), only quarterly visits (AMS(3), using visits 3 months apart), semiannual visits (AMS(6), using first, middle, and last visits only), and annual visits (AMS(12), using only the first and last visits). Comparisons of AMS(3), AMS(6), and AMS(12) with AMS(GOLD) are made using descriptive statistics. RESULTS: seventy-eight patients were included (92% women, mean age at SLE diagnosis 30.1 yrs and at study start 46.2 yrs). The mean (SD) AMS(GOLD) for the entire year was 2.05 (1.66), for AMS(3) 1.99 (1.65), for AMS(6) 2.12 (1.87), and for AMS(12) 2.08 (1.83). Mean (SD) of the absolute differences with AMS(GOLD): for AMS(3) 0.29 (0.33), for AMS(6) 0.45 (0.59), and for AMS(12) 0.61 (0.58). Differences that were < 0.5 were considered minimal while those ≥ 1 were deemed important. Comparing AMS(GOLD) to AMS(3), 82% of the differences were minimal and 3% were important. When comparing to AMS(6), 68% were minimal and 10% were important, while comparing to AMS(12), 50% were minimal and 21% were important. CONCLUSION: usual clinic visits occurring quarterly offer a good estimation of disease activity over a 1-year period and are preferred over semiannual and annual visits.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".