Toward new criteria for systemic lupus erythematosus—a standpoint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While clearly different in their aims and means, classification and diagnosis both try to accurately label the disease patients are suffering from. For systemic lupus erythematosus (SLE), this is complicated by the multi-organ nature of the disease and by our incomplete understanding of its pathophysiology. Hallmarks of SLE are the presence of antinuclear antibodies (ANA), and multiple immune-mediated organ symptoms that are largely independent. In an attempt to overcome limitations of the current sets of SLE classification criteria, a new four-phase approach is being developed, which is jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). This review attempts to delineate the performance of the current sets of criteria, the reasons for the decision for classification, and not diagnostic, criteria, and to provide a background of the current approach taken.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it