Recommendations for Frequency of Visits to Monitor Systemic Lupus Erythematosus in Asymptomatic Patients: Data from an Observational Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The aim of our study was to determine the optimal frequency of followup visits in patients with systemic lupus erythematosus (SLE). METHODS: Patients followed in the lupus clinic over a 2-year period who had at least 3 visits and at least 18 months of followup were included. At each visit patients undergo a complete history, physical examination, and laboratory evaluation. The following variables that would not have been recognized by the patient were identified: proteinuria, hematuria, pyuria, casts, low hemoglobin, leukopenia, thrombocytopenia, elevated serum creatinine, positive anti-DNA antibodies, and low complement. When one of these variables was detected, it was determined whether it was new, and whether other features of activity were present. Thus isolated new variables of interest were identified. Descriptive statistics were used. RESULTS: A total of 515 patients (89% female, 61% white) met the inclusion criteria, with an average of 6.1 ± 1.5 for a total of 3126 visits. The average length of time between visits was 3.8 ± 1.0 months. In the 515 patients, the variables of interest were the sole manifestation of SLE in 126 (24.5%) patients (in a total of 175 visits). The commonest manifestations were renal, low complement, and DNA antibodies followed by thrombocytopenia, low hemoglobin, and elevated creatinine. CONCLUSION: One in 4 patients with SLE seen over a 2-year period will have a solitary silent variable of interest that could be detected only by routine laboratory followup. Patients with mild or inactive disease should be followed with clinical and laboratory measures at 3-4 month intervals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".