<p>Use of topical bromfenac for treating ocular pain and inflammation beyond cataract surgery: a review of published studies</p>
Bibliographic record
Abstract
Topical ophthalmic nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly used to treat postoperative inflammation and pain following cataract surgery and for treatment and prophylaxis of pseudophakic cystoid macular edema (CME). Bromfenac is a brominated NSAID with strong in vitro anti-inflammatory potency. Like other ophthalmic NSAIDs, bromfenac is often used outside of the cataract surgery setting. This paper provides an overview of bromfenac's preclinical ocular pharmacology and pharmacokinetics, followed by a review of 23 published clinical studies in which various marketed bromfenac formulations were used for conditions other than cataract surgery or pseudophakic CME. These include: post-refractive eye surgery; macular edema associated with diabetes, uveitis, or retinal vein occlusion; inflammation associated with age-related macular degeneration; pain related to intravitreal injections; and other ocular anterior segment and surface disorders with an inflammatory component. The published evidence reviewed supports the safety and effectiveness of bromfenac in these additional ophthalmic indications. Bromfenac was well tolerated when given alone or in combination with intravitreal anti-vascular endothelial growth factor agents, topical corticosteroids, or topical mast-cell stabilizers. The most common adverse event reported was ocular irritation. No serious adverse events (ie, corneal epithelial disorders) were reported, although the majority of studies did not systematically evaluate potential side effects. Corneal complications, such as melts reported with diclofenac and ketorolac, were not observed with bromfenac in the studies. In summary, published study data support the clinical utility of bromfenac in various ocular disorders beyond post-cataract surgery. Additional studies are warranted to further define the potential role of bromfenac ophthalmic solution in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".