Evaluation of a Practical Approach to Diagnosis of Sjögren Disease in Clinical Practice
Bibliographic record
Abstract
OBJECTIVE: The 2016 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria (AECC) borrow from oral pathology, ophthalmology, pathology, and serology to define Sjögren disease (SjD). The objective of this study was to analyze the utility of incorporating the 2016 AECC tools into clinical practice. METHODS: A cross-sectional database with 374 patients evaluated on protocol between 1993 and 2019 at the University Health Network Multidisciplinary Sjogren's Clinic was used for the purpose of this data analysis. All patients used for this analysis had a complete evaluation, including serology, ocular surface staining, and minor salivary gland (MSG) biopsy. RESULTS: Of the 374 patients, 263 (70.3%) were diagnosed with SjD in clinic on the basis of the Schirmer test (ST), unstimulated salivary flow (USSF), and serology results alone (group A). An additional 14% were diagnosed after further assessment with ocular surface staining (ophthalmology) and MSG biopsy (ENT; group B). Group C patients did not have SjD. Groups B and C together were frequently seronegative (for antinuclear antibody and/or anti-Ro) or antimitochondrial antibody positive. Seronegative patients with abnormal ST and USSF had a positive MSG biopsy in 70% of cases. CONCLUSION: SjD could be diagnosed according to 2016 AECC in most patients on the basis of ST, USSF, and serology results where there is concern for the disease on clinical evaluation. Patients who required further testing for diagnosis had some distinctive features. This analysis provides the practicing physician with some guidelines for establishing a diagnosis of SjD in clinic.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".