When Is Evidence Enough Evidence? A Systematic Review and Meta-Analysis of the Trabectome as a Solo Procedure in Patients with Primary Open-Angle Glaucoma
Bibliographic record
Abstract
The purpose of this systematic review and meta-analysis was to examine the availability of evidence for one of the earliest available minimally invasive glaucoma surgery (MIGS) procedures, the Trabectome. Various databases were searched up to December 20, 2016, for any published studies assessing the use of the Trabectome as a solo procedure in patients with primary open-angle glaucoma (POAG). The standardized mean differences (SMD) were calculated for the change in intraocular pressure (IOP) and number of glaucoma mediations used at 1-month, 6-month, and 12-month follow-up. After screening, three studies and one abstract with analyzable data were included. The meta-analysis showed statistically significant reductions in IOP and number of glaucoma medications used at all time points. Though the Trabectome as a solo procedure appears to lower IOP and reduces the number of glaucoma medications, more high-quality studies are required to make definitive conclusions. The difficulty of obtaining evidence may be one of the many obstacles that limit a full understanding of the potential safety and/or efficacy benefits compared to standard treatments. The time has come for a thoughtful and integrated approach with stakeholders to determine optimal access to care strategies for our patients.
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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 | 0.024 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".