The Use of Prostaglandin Analogues and Cystoid Macular Edema after Uneventful Cataract Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose To evaluate whether using prostaglandin analogues (PGAs) perioperatively is associated with an increased rate of the development of clinical or subclinical cystoid macular edema (CME) after uneventful cataract surgery.Methods The PubMed, Scopus, and ScienceDirect databases were searched to June 2022 for this systematic review and meta-analysis. Two authors independently screened search results. Random-effects meta-analyses were performed to calculate the overall incidence rate and odds ratio (OR). Quality of studies was assessed using the modified Newcastle-Ottawa scale. The incidences of CME for continued vs discontinued use of PGAs perioperatively, continued use of PGAs, discontinued use of PGAs, and PGA users vs non-PGA antiglaucomatous users were main outcomes.Results Out of 544–articles, 9 studies that met the inclusion criteria were analyzed. The continued use of PGAs was not associated with an increased risk of the development of subclinical macular edema compared with discontinued use (OR = 1.32 [95% Confidence Interval (CI) = 0.49–3.51], p = .582). The overall incidence of CME was 34% (95% CI = 0.17–0.52) for continued use of PGAs and 7% (95% CI = 0.02–0.13) for discontinued use of PGAs. Using PGAs did not increase the risk of CME’s development compared with non-PGA antiglaucomatous usage (OR = 2.29 [95% CI = 0.84–6.23], p = .103)Conclusions Discontinuing treatment with PGAs during the perioperative period in eyes without any known risk factors for CME has no clinically significant effect on reducing the development of postoperative CME based on the existing studies. Further, well-designed randomized controlled trials need to be performed.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".