Cataract surgery challenges in patients with glaucoma – tips from a glaucoma specialist
Bibliographic record
Abstract
Introduction The coexistence of cataract and glaucoma is becoming increasingly common. This review provides practical guidance on optimizing cataract surgery in patients with glaucoma. By implementing patient-centered strategies, surgeons can safely and effectively manage these complex cases.Areas covered This article reviews the effects of cataract surgery on glaucoma management and the considerations necessary for eyes undergoing cataract surgery in the setting of glaucoma. Attention is given to preoperative assessment, surgical planning, intraoperative techniques, and postoperative management.Expert opinion Cataract surgery in patients with glaucoma presents unique challenges. Preoperative assessment should include a detailed review of glaucoma stability and careful consideration of the risks and benefits of cataract extraction, including visual field wipe-out. Intraoperatively, strategies to maintain anterior chamber stability and minimize stress on zonules are critical. For patients with previous glaucoma surgeries, careful timing and technique selection are crucial to preserving bleb function. Postoperatively, monitoring for IOP spikes, managing inflammation, and ensuring adherence to glaucoma therapy are essential to outcomes. Further, there is an evolving paradigm shift toward a proactive minimally invasive interventional approach to glaucoma care. Surgeons confident managing cataracts in patients with glaucoma will have an opportunity to contribute to patient-centered care by becoming familiar with MIGS procedures.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".