Optimal Timing for Intraocular Pressure Measurement Following Femtosecond Laser-Assisted Cataract Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
William J Herspiegel,1,* Brian E Yu,1,* Monali S Malvankar-Mehta,1,2,* Cindy ML Hutnik1,2,* 1Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada; 2Ivey Eye Institute, St. Joseph’s Health Care, London, Ontario, Canada*These authors contributed equally to this workCorrespondence: William J Herspiegel, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada, Email wherspiegel2026@meds.uwo.caPurpose: 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.Keywords: IOP, FLACS, preferred practice, tonometry, Goldmann applanation tonometry
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".