Uveitis Subtypes in a German Interdisciplinary Uveitis Center—Analysis of 1916 Patients
Bibliographic record
Abstract
OBJECTIVE: Studies on the epidemiology of uveitis are rare and cohorts are small. We analyzed the frequencies of classified forms of uveitis in all patients at our center. METHODS: We studied 1916 consecutive patients with inflammatory eye disease. Data were analyzed regarding associated systemic disease, infection, ocular syndromes, anatomic localization, age, and sex. RESULTS: In 59.1% of patients, a classified form of uveitis was observed: associated systemic diseases in 43.7%, the most frequent ones sarcoidosis (17.4%) and ankylosing spondylitis (16.8%); ocular syndromes in 34.3%, the most frequent HLA-B27-positive anterior uveitis (AU; 35.1%) and Fuchs uveitis syndrome (FUS; 34.3%); and infections in 22.4%, the most frequent herpetic infections (46.1%) and toxoplasmosis (31.5%). We found AU in 45.4% of patients (15.4% HLA-B27-positive AU and 11.3% FUS), intermediate uveitis in 22.9% (unclassified 53.7% and multiple sclerosis 10.3%), and posterior uveitis in 13.5% (24.7% toxoplasmosis). Panuveitis was diagnosed in 6.2% of cases (Behçet's disease 12.6%; sarcoidosis 10.9%). The remaining 12.0% of cases showed extrauveal manifestations (scleritis, episcleritis, keratitis, optic neuritis, myositis, and orbital inflammation). CONCLUSION: We describe the largest cohort to date of consecutive patients from a specialized uveitis center. The high frequency of classified disease, nearly 60% in our clinic, shows the usefulness of an interdisciplinary approach, oriented on anatomic presentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".