Effect of prophylactic nonsteroidal antiinflammatory drugs on cystoid macular edema assessed using optical coherence tomography quantification of total macular volume after cataract surgery
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of prophylactic administration of the topical nonsteroidal antiinflammatory drug (NSAID) ketorolac tromethamine 0.5% on acute (within 4 weeks of surgery) cystoid macular edema (CME) and total macular volume (TMV) in patients having phacoemulsification cataract surgery. SETTING: Department of Ophthalmology, Queen's University, Hotel Dieu Hospital, Kingston, Ontario, Canada. METHODS: This open-label nonmasked randomized (random number assignment) study comprised 106 eyes of 98 patients. Exclusion criteria included hypersensitivity to the NSAID drug class, aspirin/NSAID-induced asthma, and pregnancy in the third trimester. Ketorolac tromethamine 0.5% was administered starting 2 days before surgery and for 29 days after surgery for a total of 31 days. The outcome measure was macular swelling, which was quantified by the optical coherence tomography. RESULTS: At 1 month, there was a statistically significant difference in TMV between the control group (0.4420 mm3) and the ketorolac group (0.2392 mm3), with the ketorolac group having 45.8% less macular swelling (P = .009). Multiple linear regression with backward selection indicated a 44.3% (P = .013) and 46.1% (P = .030) reduction in macular swelling in the ketorolac group at 1 week and 1 month, respectively. CONCLUSION: Used prophylactically after cataract surgery, ketorolac 0.5% was efficacious in decreasing postoperative macular edema.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".