Associations Between Classification Criteria Items in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: A project aimed at developing new classification criteria for systemic lupus erythematosus (SLE) is based on weighted criteria that include both laboratory and clinical items. Combinations of certain symptoms may occur commonly in SLE, which provides an argument against independently counting these items. The current study was undertaken to evaluate the interrelationship between candidate criteria items in the International Early SLE cohort and in the Euro-Lupus cohort. METHODS: The International Early SLE cohort included 389 patients, who were diagnosed within 3 years prior to the study. Data on the ACR's 1997 update of the SLE revised criteria, the Systemic Lupus International Collaborating Clinics 2012 criteria, and on 30 additional items were collected. To evaluate the interrelationship of the criteria, a tetrachoric correlation was used to assess the degree of association between different manifestations in the same organ system. The correlations identified in the International Early SLE cohort were validated in the Euro-Lupus cohort. RESULTS: A few relevant correlations were observed among specific clinical cutaneous manifestations (in particular, malar rash correlated with photosensitivity, alopecia, and oral ulcers) and serologic manifestations (anti-Sm and anti-double-stranded DNA and anti-RNA polymerase, anti-Ro and anti-La, and antiphospholipid antibodies), and these results were validated in the Euro-Lupus cohort. The associations within the mucocutaneous domain, hematologic and the specific autoantibodies suggest that within a single domain only the highest ranking item should be counted to avoid overrepresentation. CONCLUSION: Some of the candidate SLE criteria cluster within domains. Given these interrelationships, multiple criteria within a domain should not be independently counted. These results are important to consider for the structure of new SLE classification criteria.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".