Comparison of trabeculectomy versus Ex-PRESS: 3-year follow-up
Bibliographic record
Abstract
AIMS: To compare the outcomes of Ex-PRESS versus trabeculectomy at 3 years. METHODS: Consenting patients aged 18-85 years with medically uncontrolled open-angle glaucoma scheduled for trabeculectomy were included in this study. 63 subjects were randomised to undergo Ex-PRESS (32) or trabeculectomy (31). Follow-up data included intraocular pressure (IOP), glaucoma medications, visual acuity (VA), complications and additional interventions. Complete success was defined as IOP between 5 and 18 mm Hg and 20% reduction from baseline without glaucoma medications, while qualified success was with or without glaucoma medications. RESULTS: Complete success at 2 and 3 years was 43% vs 42% (p=0.78) and 35% vs 38% (p=0.92) in Ex-PRESS versus trabeculectomy, respectively. Qualified success at 2 and 3 years was 59% vs 76% (p=0.20) and 52% vs 61% (p=0.43) in Ex-PRESS versus trabeculectomy, respectively. Mean IOP at 2 and 3 years was 12.5±5.1 mm Hg vs 10.3±3.7 mm Hg (p=0.07) and 13.3±4.5 mm Hg vs 11.1±4.4 mm Hg (p=0.10) for Ex-PRESS versus trabeculectomy, respectively. At 3 years, 47.6% of Ex-PRESS and 50% of trabeculectomy patients were on glaucoma medications (p=1.00). No difference in VA was found after 3 years (logarithm of minimum angle of resolution 0.43±0.4 vs 0.72±0.8 for Ex-PRESS vs trabeculectomy, p=0.11). When excluding patients who underwent reoperation VA was better in the Ex-PRESS group at 1, 2 and 3 years. There were no complications after the first year in either group. CONCLUSIONS: We found no difference in success rates, mean IOP or other secondary outcomes between Ex-PRESS and trabeculectomy after 3 years of follow-up. TRIAL REGISTRATION NUMBER: NCT01263561; post results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".