Uveitis in Adult Patients with Rheumatic Inflammatory Autoimmune Diseases at a Tertiary-care Hospital in Mexico City
Bibliographic record
Abstract
OBJECTIVE: Our aim is to describe the frequency of uveitis associated with rheumatic inflammatory autoimmune diseases (RIAD) in adult patients admitted to the Rheumatology Department at a tertiary-care hospital in Mexico City. We also describe the clinical features, seasonal distribution, treatment, and ocular complications associated with this disease. METHODS: We reviewed 1332 charts of patients with RIAD and selected those that had a diagnosis of uveitis. We obtained the following data: age, sex, type of uveitis and relationship with diagnosis of RIAD, recurrences, seasonal distribution, treatment, and residual visual deficit. RESULTS: We found 57 (4.27%) cases of uveitis in 1332 charts, including 38 men and 19 women (M:F ratio 2:1), aged 47 ± 16 years. Nongranulomatous acute anterior uveitis (NGAAU) comprised 90.52% of cases (52/57). In 64.91% of cases (37/57), uveitis preceded the diagnosis of RIAD by 12 ± 9 years, more frequently in winter (35.96%; p = NS). Uveitis was found in 40/93 patients with ankylosing spondylitis (AS), in 7/11 patients with relapsing polychondritis (RP), in 8/16 patients with Behçet's disease, in 1/16 patients with polyarteritis nodosa, and in 1/590 patients with rheumatoid arthritis (RA). Ninety-six percent of the patients were treated with steroids. Upon a mean followup of 60 days (range 7-4745 days), reduction of visual acuity (≤ 20/200) was associated with recurrence of uveitis in 3/7 cases with AS, in 4/8 cases with Behçet's disease, in 3/7 with RP, and in 1 case of uveitis and seronegative RA. CONCLUSION: NGAAU frequently precedes RIAD and is found predominately in men, with a tendency to occur in winter.
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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 | 0.001 | 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) | 0.002 | 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".