Association Between Glandular Infiltrate and Leukopenia in Sjögren Syndrome (SS): Data From the Italian Research Group on SS (GRISS)
Bibliographic record
Abstract
We read with great interest the paper by Sharma, et al 1, who analyzed a cohort of patients with primary Sjögren syndrome (pSS), evaluating differences between a subgroup of subjects with positive minor salivary gland (MSG) biopsy compared to patients with a negative MSG biopsy. The authors found a higher prevalence of anti-La positivity and increased levels of IgG in the former group and identified a significant upregulation of type I interferon (IFN)–regulated genes in a sample group of patients with positive MSG biopsy. These interesting data prompted us to analyze serological and immunological features in the same 2 groups in our very large cohort of 1706 pSS patients recruited in the multicenter GRISS (Italian research group on SS) study. To be able to compare the data with those by Sharma, et al 1, we included only patients with positive anti-Ro antibodies who underwent MSG biopsy and compared the subjects with a positive result, defined as a … Address correspondence to Prof. R. Gerli, Rheumatology Unit, Department of Medicine, University of Perugia, P.le Menghini 1, 06129 Perugia, Italy. Email: roberto.gerli{at}unipg.it.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".