Optimal Timing for Intraocular Pressure Measurement Following Phacoemulsification Cataract Surgery: A Systematic Review and a Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it