Uveitis Subtypes in a German Interdisciplinary Uveitis Center—Analysis of 1916 Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it