Epidemiology of cutaneous involvement in Sjögren syndrome: Data from three French pSS populations (TEARS, ASSESS, diapSS)
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and significance of dermatological disorders in primary Sjögren syndrome (pSS). METHODS: We used 2 pSS French cohorts (ASSESS, in which prevalence of skin disorders in 395 patients was evaluated; and diapSS, in which 76 on 139 pSS patients had an examination by a dermatologist) and baseline data of the TEARS randomized trial (110 patients with recent or active pSS treated with rituximab or placebo and evaluated for skin dryness using a visual analogue scale (VAS) out of 100). RESULTS: Skin manifestations included in the EULAR Sjögren syndrome disease activity index (ESSDAI) were rare in the ASSESS cohort (n=16/395, 4.1%, mainly purpuras; only 3 had high activity), but they were associated with activity in the other ESSDAI domains (peripheral neurological (P<0.001), muscular (P<0.01), haematological (P<0.05), biological (P<0.05), history of arthritis (P<0.01), splenomegaly (P<0.05) and higher gamma globulin levels (P<0.01)). In the diapSS cohort, compared to pSS patients not receiving a dermatological consultation, the pSS patients who had a dermatological consultation had significantly more dermatological involvement outside the ESSDAI score [38.2% (29/76) versus 15.9% (10/63); P<0.01]. The TEARS study showed a high prevalence of cutaneous dryness (VAS>50; 48.2%) and found that patients with dry skin had higher VAS pain (P<0.01) and drought (P<0.01) scores. CONCLUSION: ESSDAI skin activity is rare and associated with hypergammaglobulinemia and ESSDAI activity. Systematic dermatological examination is informative for non-specific lesions. The most common skin disorder is skin dryness, which is associated with a higher pain and overall subjective dryness.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".