Conjunctival Complications Related to Ahmed Glaucoma Valve Insertion
Bibliographic record
Abstract
PURPOSE: Conjunctival complications may occur after glaucoma drainage device surgery. We analyzed the frequency, risk factors, management and outcomes of Ahmed glaucoma valve (AGV)-related conjunctival complications. METHODS: Retrospective review of postoperative conjunctival complications in patients undergoing AGV insertion. Only subjects with ≥1-year follow-up were included. RESULTS: The charts of 158 subjects with a median age of 64±16.2 years were reviewed. Median follow-up was 43.5 months (range, 12 to 103 mo). Fifty-three (33.5%) wound dehiscences and 14 (8.9%) device exposures were diagnosed 31.6±35.7 and 996±735 days after procedure, respectively (P<0.001). Ninety-one subjects (57.6%) had no conjunctival complications. This uncomplicated group used 3.3 (±1.1) [95% confidence interval (CI): 3.07, 3.51] hypotensive medications before surgery as compared with 3.8 (±1.1) (95% CI: 3.48, 4.10) and 3.9 (±0.9) (95% CI: 3.36, 4.36) for dehiscence and exposure groups, respectively (P=0.01). The inferonasal quadrant was associated with the highest rate of dehiscences (4/7, 57.1%) (95% CI: 18.4, 90.1), followed by the inferotemporal quadrant (30/65, 46.2%) (95% CI: 33.1, 58.2), the superotemporal (15/61, 24.6%) (95% CI: 12.9, 33.8), and the superonasal (4/25, 16%) (95% CI: 10.9-52.0; P<0.0073). There were no differences in dehiscence and exposure rates between limbal versus fornix-based approaches (P=0.54; 95% CI: 24.8-44.9, 24.4-45.7, 5.9-19.6, 3-45.1, respectively). Forty-eight (90.6%) dehiscent wounds resolved with conservative management and 5 were resutured, whereas all exposed devices were managed surgically. CONCLUSIONS: Conjunctival dehiscence is usually a benign, common complication after AGV insertion. It does not need repair as long as the tube is well covered. AGV tube or plate exposures are less common, occur later and were promptly repaired as per current practice. Important factors predisposing to these problems include a greater number of preoperative hypotensive medications and the implantation quadrant.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".