Performance of the 2016 ACR/EULAR SS classification criteria in patients with secondary Sjögren's syndrome.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the performance of the 2016 ACR-EULAR classification Sjögren's syndrome (SS) criteria for classifying patients with secondary SS. METHODS: We randomly selected 300 patients with systemic lupus erythematosus, rheumatoid arthritis and scleroderma, as well as 50 with primary SS. SS diagnosis was established by two independent rheumatologists and was based on the combination of symptoms, signs, diagnostic tests and medical chart review. We evaluated the fulfillment of the 2002 AECG, 2012 ACR and 2016 ACR/EULAR criteria, and their performance using as the gold standard the clinical diagnosis. RESULTS: We identified 154 patients with a clinical (definitive/probable) SS diagnosis, 95 patients (61.7%) fulfilled the AECG, 96 patients (62.3%) the ACR and 90 (58.4%) the 2016 ACR/EULAR criteria. Among the subset with definitive SS clinical diagnosis (n=99), 83 patients (83.8%) fulfilled the AECG, 77 (77.7%) the ACR and 79 (79.7%) the 2016 ACR/EULAR criteria. The concordance rate between the clinical diagnosis (definitive/probable) and the AECG, ACR and 2016 ACR/ EULAR criteria was κ=0.58, κ=0.55 and κ=0.60, respectively. The 2016 ACR/EULAR criteria showed the best AUCs results (0.87 definitive/probable diagnosis, 0.90 definitive diagnosis), followed by the AECG (0.82 definitive/probable diagnosis, 0.85 definitive diagnosis) and ACR (0.80 definitive/probable diagnosis, 0.79 definitive diagnosis) criteria. As a sensitivity analysis, the results were similar when excluding patients with primary SS. CONCLUSIONS: Our study provides further evidence that the 2016 ACR/EULAR criteria are applicable in the setting of secondary SS.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".