Performance of the 2019 ACR/EULAR classification criteria for IgG4-related disease and clinical phenotypes in a Spanish multicentre registry (REERIGG4)
Bibliographic record
Abstract
OBJECTIVES: Several IgG4-related disease (IgG4-RD) phenotypes have been proposed and the first set of classification criteria have been recently created. Our objectives were to assess the phenotype distribution and the performance of the classification criteria in Spanish patients as genetic and geographical differences may exist. METHODS: We performed a cross-sectional multicentre study (Registro Español de Enfermedad Relacionada con la IgG4, REERIGG4) with nine participating centres from Spain. Patients were recruited from November 2013 to December 2018. The 2019 American College of Rheumatology/European League Against Rheumatism classification criteria (AECC) were used. RESULTS: We included 105 patients; 88% had Caucasian ethnicity. On diagnosis, 86% met the international pathology consensus while 92% met the Japanese comprehensive criteria. The phenotype distribution was head and neck 25%, Mikulicz and systemic (MS) 20%, pancreato-hepato-biliary (PHB) 13%, retroperitoneal and aorta (RA) 26%. Sixteen per cent had an undefined phenotype. Seventy-seven per cent of the cases met the AECC. From the 24 patients not meeting the AECC, 33% met exclusion criteria, and 67% did not get a score ≥20 points. Incomplete pathology reports were associated to failure to meet the AECC. CONCLUSIONS: The PHB phenotype was rare among Spanish IgG4-RD patients. The MS phenotype was less frequent and the RA phenotype was more prevalent than in other, Asian patient series. An undefined phenotype should be considered as some patients do not fall into any of the categories. Three quarters of the cases met the 2019 AECC. Incomplete pathology reports were the leading causes of failure to meet the criteria.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".