Pattern and Visual Prognostic Factors of Behcet’s Uveitis in Northwest Iran
Bibliographic record
Abstract
Purpose: To investigate the pattern of ocular involvement in Behcet's disease (BD) with predictors of patients' final state of vision. Methods: This historical cohort encompassed the clinical records of 200 patients diagnosed according to the International Criteria for BD (ICBD), over a period of 17 years between 2004 and 2021. Results: The prevalence of Behcet's uveitis (BU) was more common in females and patients in the fourth decade of life. Ninety-five patients (47.5%) had evidence of ocular involvement in the initial ophthalmologic evaluation, and 171 patients (85.5%) manifested evidence of BU during the follow-up visits of which bilateral non-granulomatous panuveitis was the most common anatomical pattern of involvement (32.9%) followed by posterior (27.6%), anterior (26.5%), and intermediate (13.8%) uveitis. The prevalent accompanying signs were oral aphthous (67%), skin lesions (29%), and genital ulcers (19.5%). Cystoid macular edema (CME) was the most frequent ocular complication (62%), followed by cataract (57.5%) and epiretinal membranes (ERM) (36.5%). Univariate analysis showed the following determinants: male gender, younger age at onset, panuveitis, posterior uveitis, retinal vasculitis, and longer duration of uveitis as poorer visual prognostic factors of the disease. Multivariate analysis demonstrated a higher chance of poor visual prognosis of BD in patients with panuveitis, posterior uveitis, retinal vasculitis, and longer duration of uveitis. Conclusion: This cohort study demonstrated an overview on epidemiological patterns of BU along with the visual prognostic factors in Iranian patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".