Bibliographic record
Abstract
To the Editor: We offer the following comments on the recent article by Baer, et al 1. Overlap of systemic lupus erythematosus (SLE) and Sjögren’s syndrome (Sj) certainly does occur and we are pleased that this has now been recognized and characterized. We wish to add our observations suggesting that this overlap, which we refer to as “sjrupus,” responds well to rituximab. Patient 1. A woman who in 2000 at age 28 years developed autoimmunity characterized by sicca syndrome with positive SSA and SSB antibodies. There were lupus-like skin changes of discoid disease without SLE antibodies. Hydroxychloroquine (HCQ) was efficacious for arthralgia and later leflunomide was somewhat efficacious for lachrymal and salivary disease. In July 2005, coincidental with administration of Depo Provera, she developed severe bulky polysynovitis, lupus rashes on the face, malaise, and fatigue. Anti-dsDNA antibodies were off-scale positive along with SM, rheumatoid factor (RF) in IgA, M and G subclasses, SSA … Address correspondence to Dr. Handler; E-mail: rphandler{at}gmail.com
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".