Autoantibodies in SLE: prediction and the <i>p</i> value matrix
Bibliographic record
Abstract
Autoantibodies (AA) and antinuclear antibodies (ANA) serve as key diagnostic and classification criteria for systemic lupus erythematosus (SLE). More than 200 different AA have been reported in SLE, although only a handful (<20) are considered "mainstream" because they are widely and routinely used in diagnostic, research and clinical medicine. Although the vast majority of AA have been relegated to the diminished status of "orphan" AA, some serve as predictors of SLE because they first appear in very early or subclinical SLE. Some AA are pathogenic, whereas others are thought to protect against or ameliorate disease progression and, hence, taken together can be used as predictive biomarkers of prognosis. Although studies have shown that specific AA are detected in the preclinical phase of SLE and are biomarkers of increased risk of developing the disease, AA are currently not widely used to predict very early SLE in individuals who have low pretest probability of disease. With the advent of multianalyte arrays with analytic algorithms, emerging evidence indicates that when certain combinations of biomarkers, such as the interferon signature and stem cell factor accompany AA and ANA, the predictive power for SLE is markedly increased.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".