Gel Stent Implantation—Recommendations for Preoperative Assessment, Surgical Technique, and Postoperative Management
Bibliographic record
Abstract
The surgical management of glaucoma offers the potential to lower intraocular pressure (IOP) independent of patients’ compliance with their medication regimen. Procedures such as trabeculectomy and tube shunt placement often yield large magnitudes of IOP reduction, but may be associated with short- and long-term complications. Microinvasive glaucoma surgery (MIGS) offers an alternative surgical approach that is inherently less invasive; however, most devices that fit in this category are associated with a lesser degree of IOP-lowering efficacy compared with traditional glaucoma surgeries. A newer MIGS device, a gel stent that facilitates drainage to the subconjunctival space, appears to offer similar IOP reduction to trabeculectomy, but with much less tissue manipulation; better predictability; and less sight-threatening complications, thus making it a potentially safer and more predictable surgical option in appropriate patients. The following proposed protocol, based on evidence-based practices and augmented where necessary by the opinions of experienced surgeons, provides guidance for the pre-, intra-, and postoperative management of patients receiving a gel stent implant. The goal of this protocol is to provide a framework for better patient selection and preparation, surgical pearls, and how best to assess and manage patients in the postoperative period.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".