Do Non-Steroidal Anti-Inflammatory Drugs Delay Posterior Capsule Opacification After Phacoemulsification in Children? A Randomized, Prospective Controlled Trial
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy of topical ketorolac for the prevention of posterior capsule opacification (PCO) in pediatric cataract surgery. MATERIALS AND METHODS: The eyes were prospectively and randomly assigned to receive postoperative 3-month topical ketorolac 0.5% drops with intact posterior capsule (group 1) or primary posterior capsulotomy combined with anterior vitrectomy (group 2). All children had uneventful corneal small-incision phacoemulsification with a primary implantation of a foldable acrylic posterior chamber intraocular lens (IOL). The frequency and timing of severe PCO was evaluated for each group and documented by slit-lamp examination and photography. RESULTS: A total of 38 eyes of 27 children (15 girls, 12 boys) were included in the study. Among them 16 children had unilateral and 11 had bilateral surgery. All cataracts were developmental cases diagnosed between 1 and 7 years of age. There were 20 eyes in group 1 and 18 eyes in group 2. Mean pediatric age at surgery was 38.1 months (12-72 months) in group 1 and 34.2 months in group 2 children (12-78 months) (p>0.05). Overall mean follow up was 26.3 months (6-48 months). Clinically significant PCO that finally needed neodymium:YAG laser application developed in four eyes (20.0%) in group 1 and in two eyes (11.1%) in group 2, and the difference was not statistically significant (chi-square test, p >0.05). CONCLUSIONS: Long-term postoperative use of topical ketorolac drops with the preservation of posterior capsule was found to be effective for the prevention of PCO in pediatric cataract surgery, at least during the period of this study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".