Ab interno trabeculectomy: Outcomes in exfoliation versus primary open-angle glaucoma
Bibliographic record
Abstract
PURPOSE: To compare outcomes in exfoliation glaucoma versus primary open-angle glaucoma (POAG) after ab interno trabeculectomy alone (Trabectome) or in combination with cataract surgery and intraocular lens (IOL) implantation. SETTING: Trabectome Study Group institutions. DESIGN: Prospective nonrandomized cohort study. METHODS: Outcomes included intraocular pressure (IOP), glaucoma medications, complications, secondary procedures, and success, defined as no secondary surgery and IOP less than 21 mm Hg and a greater than 20% reduction from baseline. RESULTS: In the ab interno trabeculectomy-alone group, the mean preoperative IOP was 29.0 mm Hg ± 7.5 (SD) in exfoliation glaucoma cases and 25.5 ± 7.9 mm Hg in POAG cases (P<.01). At 1 year, the mean decrease in IOP was -12.3 ± 8.0 mm Hg and -7.5 ± 7.4 mm Hg, respectively (P<.01); the secondary procedure rate was 20.9% and 34.9%, respectively (P=.02); and the cumulative probability of success was 79.1% and 62.9%, respectively (P=.004). In the combined ab interno trabeculectomy-IOL group, the mean preoperative IOP was 21.7 ± 8.4 in exfoliation glaucoma cases and 19.9 ± 5.4 mm Hg in POAG cases (P=.06). At 1 year, the mean decrease in IOP was -7.2 ± 7.7 and -4.1 ± 4.6, respectively (P<.01); the secondary procedure rate was 6.7% and 6.1%, respectively (P=.88); and the cumulative probability of success was 86.7% and 91.0% (P=.73), respectively. CONCLUSION: Ab interno trabeculectomy using this new incisional procedure safely lowered IOP to the mid teens, with an overall greater reduction in exfoliation glaucoma and improved success when combined with cataract surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".