Role of specialty care in the management of patients with systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: To determine the role of rheumatologists in the management of systemic lupus erythematosus (SLE). METHODS: The lupus clinic database was searched for patients with 3 consecutive visits (every 3-4 months) of which the first 2 visits recorded a SLE Disease Activity Index (SLEDAI) of 0. The clinic notes were examined by a physician blinded to the SLEDAI score at the third visit. The physician classified the rheumatologist's action by the following scale: 1 = no change, 2 = closer followup, 3 = new investigations, 4 = increase medications, 5 = lower medications. All interventions (2-5) were further scored as being related to or independent of SLE. RESULTS: Of the 142 SLE patients identified, 70 patients remained inactive (SLEDAI = 0) and 72 patients experienced flare (SLEDAI > 0) at the third visit. In total, 74% of patients, regardless of the status of disease activity, required intervention; 96% of interventions in patients with clinical flare, 72% with serological flare, and 63% with inactive disease were due to management of SLE. The most frequent intervention related to SLE in patients with clinical flare was increasing medication, while in inactive SLE lowering medication was the most common intervention. CONCLUSION: Even after a period of relative disease quiescence the majority of patients with lupus require active intervention during a subsequent routine clinic visit. Most interventions are related to the management of SLE. Therefore ongoing monitoring by rheumatologists in the management of lupus seems prudent.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".