Study of Flare Assessment in Systemic Lupus Erythematosus Based on Paper Patients
Bibliographic record
Abstract
OBJECTIVE: To determine the level of agreement of disease flare severity (distinguishing severe, moderate, and mild flare and persistent disease activity) in a large paper-patient exercise involving 988 individual cases of systemic lupus erythematosus. METHODS: A total of 988 individual lupus case histories were assessed by 3 individual physicians. Complete agreement about the degree of flare (or persistent disease activity) was obtained in 451 cases (46%), and these provided the reference standard for the second part of the study. This component used 3 flare activity instruments (the British Isles Lupus Assessment Group [BILAG] 2004, Safety of Estrogens in Lupus Erythematosus National Assessment [SELENA] flare index [SFI] and the revised SELENA flare index [rSFI]). The 451 patient case histories were distributed to 18 pairs of physicians, carefully randomized in a manner designed to ensure a fair case mix and equal distribution of flare according to severity. RESULTS: The 3-physician assessment of flare matched the level of flare using the 3 indices, with 67% for BILAG 2004, 72% for SFI, and 70% for rSFI. The corresponding weighted kappa coefficients for each instrument were 0.82, 0.59, and 0.74, respectively. We undertook a detailed analysis of the discrepant cases and several factors emerged, including a tendency to score moderate flares as severe and persistent activity as flare, especially when the SFI and rSFI instruments were used. Overscoring was also driven by scoring treatment change as flare, even if there were no new or worsening clinical features. CONCLUSION: Given the complexity of assessing lupus flare, we were encouraged by the overall results reported. However, the problem of capturing lupus flare accurately is not completely solved.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".