Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the validation of the European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) 2019 classification criteria for systemic lupus erythematosus (SLE). RECENT FINDINGS: Positive antinuclear antibodies, which constitute the obligatory entry criterion of the EULAR/ACR criteria, were found in the vast majority of SLE patients worldwide, with 97% (94-100%) of patients antinuclear antibodies positive in studies investigating EULAR/ACR criteria performance. Combined over the publications, EULAR/ACR criteria sensitivity was 92% (range 85-97%). Specificity varied more relevantly, with the publications published after the EULAR/ACR 2019 criteria showing 93% (83-98%) specificity. Of particular relevance is the good performance of the EULAR/ACR criteria seen in pediatric SLE as well as in early SLE. SUMMARY: The new classification criteria have been investigated in an impressive number of cohorts worldwide, adding to the data from the EULAR/ACR criteria project cohort. It is critical to strictly keep to the attribution rule, that items are only counted if there is no more likely alternative explanation than SLE, the domain structure, where only the highest weighted item in a domain counts, and the limitation to highly specific tests for antibodies to double-stranded DNA.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".