Optimal Timing for Intraocular Pressure Measurement Following Femtosecond Laser-Assisted Cataract Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Femtosecond laser-assisted cataract surgery (FLACS) has increasingly been adopted worldwide. Lagging behind is evidence-based consensus regarding optimal timing for intraocular pressure (IOP) measurement following FLACS. The purpose of this study was to determine if enough evidence currently exists to guide best practice. Methods: A comprehensive literature search was performed on MEDLINE and EMBASE until February 6th, 2023. Articles reporting IOP measurements following uncomplicated FLACS were screened. For change in IOP at various post-operative timepoints, standardized mean difference (SMD) was calculated as the mean difference in IOP from baseline. Risk of Bias Assessment was conducted following data extraction. Results: The meta-analysis incorporated six randomized clinical studies involving a total of 1356 eyes from 1032 participants. Post-operative day one was the only timepoint with a non-significant increase in IOP (SMD = -0.08 [95% CI: -0.41 to +0.24]) compared to the 7-days, 30-days, 60 to 90-days, and 180-days follow-up periods. All studies except one utilized an ophthalmic viscosurgical device (OVD) in their procedure; this was the only publication that reported a decrease in IOP from baseline within the 1-day follow-up period. Conclusion: The results suggest that the optimal time to measure IOP is within the first 24 hours after FLACS. However, these findings are limited by a small study sample. Future prospective clinical trials may be beneficial to determine if specific timepoints within the first 24 hours exist to optimize outcomes and patient reported experiences.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".