Optimal Timing for Intraocular Pressure Measurement Following Phacoemulsification Cataract Surgery: A Systematic Review and a Meta-Analysis
Bibliographic record
Abstract
Post-operative increases in intraocular pressure (IOP) are a frequent complication following phacoemulsification cataract surgery. Assessment of IOP is an essential element in post-operative checks. Despite this, guidance regarding the optimal timing remains vague. The purpose of this meta-analysis was to determine the current status of evidence that may help guide best practice regarding the optimal time following phacoemulsification cataract surgery to measure IOP. A comprehensive literature search was performed on MEDLINE and EMBASE. In two stages, independent reviewers screened articles that reported IOP measurements following uncomplicated cataract surgery. Risk of Bias Assessment was conducted following data extraction. The meta-analysis incorporated 57 randomized clinical studies involving a total of 6318 participants and 7089 eyes. Post-operative hour one had a significant decrease in IOP from baseline, while hour two had a non-significant increase. Post-operative hours four, six, and eight were the only timepoints to have a significant increase in IOP. Finally, post-operative day one had no significant change in IOP, while day two had a non-significant decrease. These results suggest that the optimal time to measure IOP is within the first 4-8 h following phacoemulsification cataract extraction. Taking measurements too soon or too late could result in missed IOP spikes.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| 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".