Efficacy and vitreous levels of topical NSAIDs
Bibliographic record
Abstract
INTRODUCTION: Nonsteroidal anti-inflammatory drugs (NSAIDs) are one of the most commonly prescribed medications and are routinely used for their analgesic, antipyretic, and anti-inflammatory properties. Because of their potent cyclooxygenase-inhibitory activity, they can inhibit pro-inflammatory prostaglandin synthesis, leading to complex inflammatory cascades. NSAIDs have been broadly used systemically for many decades and have recently become commercially available in the form of topical ophthalmic formulations. NSAIDs are weak acids with pKa values mostly between 3.5 and 4.5 and are poorly water-soluble. New, aqueous ophthalmic solutions of NSAIDs that afford better tissue penetration have recently been developed. In ophthalmological practice, topical NSAIDs are mostly used to stabilize pupillary dilation during intraocular surgery, manage postoperative pain and inflammation, and treat pseudophakic cystoid macular edema. AREAS COVERED: This review focuses on the vitreous penetration of topical NSAIDs and their potential clinical applications in the treatment of retinal diseases. EXPERT OPINION: A growing body of evidence suggests that NSAIDs may be beneficial in the treatment of age-related macular degeneration, diabetic retinopathy, and ocular tumors. Recent studies from our group and other authors have shown that the vitreous levels of NSAID exceed the median inhibitory concentration, which can significantly decrease vitreous PGE2 levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".