Comparison between MIGS with trabeculectomy in the management of open-angle glaucoma with cataract: A systematic review
Bibliographic record
Abstract
Age is an important risk factor for both glaucoma and cataract. As global life expectancy continues to rise, the prevalence of concomitant open-angle glaucoma (OAG) and cataracts is increasing. Currently, there is a lack of definitive consensus on the optimal management approach for such individuals. Conventionally, trabeculectomy (Trab) in combination with phacoemulsification is the preferred method. Recent developments in microinvasive glaucoma surgery (MIGS), which offer similar results with fewer complications, provide a new possible approach to this condition. This study aimed to assess the current knowledge of combination surgery in patients with cataract and OAG to provide a comprehensive understanding and help its implementation in clinical settings. A comprehensive systematic search was conducted in May 2021 on five databases (MEDLINE, Embase, SCOPUS, Proquest, EBSCO, and Cochrane Library). The results were filtered for English and human studies but not publication year. All studies published up to May 2021 were reviewed. Newcastle-Ottawa Scale and PEDro scale were used to screen studies for risk of bias where appropriate. Four studies satisfied the inclusion criteria and were subsequently added in this review. Study designs consisted of one RCT and three descriptive studies. Appropriate assessment tools were used; these studies demonstrated moderate to good quality. Postoperative mean IOP, IOP reduction, and qualified success rates were comparable in the Phaco-MIGS (Phaco-endoscopic cyclophotocoagulation (ECP), Phaco-ab interno trabeculectomy (AIT)) and Phaco-Trab group. Severe complication was reported only in the latter. Phaco-MIGS (in particular, trabectome) shows excellent promise as an option for individuals with OAG and concomitant cataract; further research through RCT is required to validate these findings.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".