THE TAIWAN COLLEGE OF RHEUMATOLOGY CONSENSUS FOR THE MANAGEMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV260 / #333 Poster Topic: AS24 - SLE-Treatment Background/Purpose Systemic lupus erythematosus (SLE) exhibits diverse clinical presentations and requires regionally-specific management strategies. Building upon established international guidelines, The Taiwan College of Rheumatology developed the first set of recommendations for SLE management to focus on the unmet needs in clinical practice in Taiwan. This consensus aims to empower healthcare professionals and optimize patient outcomes in Taiwan. Methods Rheumatologists from various practicing institutions formed a 15-member panel, through a modified Delphi process, developed consensus statements, which encompasses various aspects of SLE care in Taiwan, including screening and diagnosis; disease monitoring; treatment strategies; and pregnancy. The expert panel reviewed and refined statements through 2 meetings with anonymous voting based on a 5-point Likert scale. Consensus is defined as ≥75% agreement to the proposed statements. Results In total, 32 statements achieved consensus. These statements incorporate the latest scientific evidence with insights from Taiwanese experts to address the unique disease characteristics and challenges faced by patients in the region. Conclusions These could serve as a guide to specialists, family physicians, specialty nurses, and other healthcare professionals in Taiwan in the management of SLE.
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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 | 0.046 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".