Efficacy and Safety of Aurolab Aqueous Drainage Implant Compared With Baerveldt Glaucoma Implant for Refractory Glaucoma at One Year: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background: Glaucoma stands as a prominent contributor to irreversible vision impairment on a global scale. For decades, the Baerveldt Glaucoma Implant (BGI) has been used to treat refractory glaucoma. Yet, the cost‐effective Aurolab Aqueous Drainage Implant (AADI) has gained clinical attention as a viable alternative for managing glaucoma. Objective: The purpose of this study was to evaluate and compare the efficacy and safety of AADI and BGI in the treatment of refractory glaucoma. Methods: Following PRISMA guidelines, we conducted a systematic search of multiple databases, identifying relevant comparative studies assessing AADI versus BGI in patients with refractory glaucoma. Key outcomes included postoperative IOP, surgical success rates, antiglaucoma medication reduction (AGMR), and complication rates. Quality assessment was performed using the Newcastle–Ottawa Scale (NOS). Results: Three studies comprised a total of 176 individuals with refractory glaucoma, with 107 patients receiving the AADI and 69 patients receiving the BGI. The meta‐analysis revealed a statistically borderline significant reduction in postoperative IOP favoring the AADI at 3 months (mean difference [MD] = −2.74, p = 0.05). There was no significant difference in the MD of AGMR between the AADI and BGI groups. The rates of total complications and surgical success did not differ significantly between the AADI and BGI groups. Conclusion: AADI demonstrates promising results in reducing IOP at 3 months compared to BGI, with comparable surgical outcomes and complication rates over the long term. Further studies with larger samples are warranted to validate these findings and assess cost‐effectiveness, particularly in developing countries.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".