Effect of Prophylactic Aqueous Suppression on Ahmed Glaucoma Valve Surgery Success
Bibliographic record
Abstract
Purpose: To evaluate the effect of prophylactic aqueous suppressants immediately post-Ahmed glaucoma valve (AGV) surgery on the rate of hypertensive phase and success. Methods: Retrospective case–control study of 80 eyes with refractory glaucoma undergoing AGV surgery. Forty eyes in the intervention group (preoperative aqueous suppressants continued postoperatively) and 40 in the control group (all glaucoma drops stopped after surgery and reintroduced as required) were included in this study. Patients were followed for 1 year. Data collected included intraocular pressure (IOP), number of glaucoma medications, and number of eyes requiring further IOP lowering surgery. The frequency of hypertensive phase and 1-year success was compared between the groups. Results: Hypertensive phase occurred in 22.5% of the intervention group compared to 42.5% of the control group; however, this difference was not statistically significant ( P = 0.06). Success at 1 year (IOP ≤21 mmHg but ≥5 mmHg and 20% reduction from baseline without additional surgery) was similar in each group: 77.5% in the intervention group and 62.5% in the control group ( P = 0.22). However, at 1 year, significantly more eyes in the intervention group had an IOP ≤17 mmHg (95% vs. 80%, P = 0.04). The mean time interval to a second IOP lowering procedure was significantly shorter in the control group ( P < 0.005). Conclusions: With prophylactic preoperative aqueous suppressants, more eyes achieved an IOP of ≤17 mmHg. The time interval to repeat the glaucoma procedure was significantly shorter in the control group.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".