Anterior uveitis for the comprehensive ophthalmologist
Bibliographic record
Abstract
Anterior uveitis presents a diagnostic challenge due to its wide array of etiologies and clinical manifestations. This narrative review aims to equip general ophthalmologists with a comprehensive understanding of anterior uveitis epidemiology, diagnosis, and treatment. Particular emphasis is placed on developing a tailored and stepwise strategy, rather than a one-size-fits-all approach, for the workup and treatment of anterior uveitis. Chest radiography and serologic testing for syphilis, human leukocyte antigen B27, and angiotensin-converting enzyme are appropriate routine investigations in cases of severe, bilateral, recurrent, or chronic anterior uveitis. Additional testing should be guided by clinical findings and regional epidemiology, especially when considering expensive and invasive modalities. Investigations that are obtained in the absence of clinical and epidemiologic orientation are of limited utility and incur significant costs to patients and health care systems. Most cases of anatomically isolated anterior uveitis resolve with topical corticosteroids, but some patients require escalation to systemic immunomodulatory therapy (IMT). IMT should be considered in patients who respond poorly to corticosteroids, develop side effects related to corticosteroids that limit their use, require high doses to maintain disease remission, or have concomitant systemic inflammatory disease. Comprehensive ophthalmologists should feel comfortable comanaging patients that require conventional disease-modifying antirheumatic drugs/antimetabolite therapy (i.e., methotrexate, azathioprine, and mycophenolate mofetil) with rheumatologists and providing guidance on ocular dosing. When uveitis quiescence cannot be achieved despite maximally tolerated antimetabolite therapy, patients should be referred to a uveitis specialist for consultation and consideration of IMT escalation. The timing of uveitis referral may depend on local factors specific to health care jurisdictions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".