Gonioscopy-assisted transluminal trabeculotomy is an effective surgical treatment for uveitic glaucoma
Bibliographic record
Abstract
BACKGROUND: To assess the efficacy and safety of gonioscopy-assisted transluminal trabeculotomy (GATT) in uveitic glaucoma (UG). METHODS: A retrospective interventional case series in which 33 eyes of 32 patients with UG underwent GATT with or without concomitant cataract extraction and intraocular lens implantation (CE/IOL) at three Canadian treatment centres from October 2015 to 2020. The main outcome measure was surgical success defined as an intraocular pressure (IOP) ≤18 mm Hg and at least one of the following: IOP within one mm Hg of baseline on fewer glaucoma medications as compared with baseline or a 30% IOP reduction from baseline on the same or fewer medications. Secondary outcome measures were IOP, medication usage and surgical complications. RESULTS: Mean patient age (mean±SD) was 49±16 years (range: 18-79) and 44% were female. GATT was performed as a standalone procedure in 52% of cases and the remainder were combined with CE/IOL. Surgical success was achieved in 71.8% (SE: 8.7%) of cases. Mean preoperative IOP (±SD) was 31.4±10.8 mm Hg on a median of 4 medications. 59% of patients were on oral carbonic anhydrase inhibitors (CAIs) prior to surgery. After 1 year, average IOP was 13.8 mm Hg on a median 1 medication, with 6% of patients being on oral CAIs. No sight threatening complications occurred during surgery or follow-up. CONCLUSION: GATT is an effective surgical strategy in the management of UG. This microinvasive conjunctival-sparing procedure should be considered early in these patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".