Trabeculotomy in the Treatment of Pediatric Uveitic Glaucoma
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy and safety of trabeculotomy in the treatment of pediatric uveitic glaucoma (UG). MATERIALS AND METHODS: We retrospectively reviewed all cases that underwent trabeculotomy for pediatric UG at our center between 2008 and 2014. Up to 2 trabeculotomies per eye were performed in patients with medically controlled uveitis. Surgical success was defined as final intraocular pressure <22 mm Hg and ≥6 mm Hg after 1 or 2 trabeculotomies, with or without medications. Kaplan-Meier survival analyses were done. RESULTS: A total of 33 trabeculotomies were performed in 28 eyes of 22 patients. Diagnoses included UG associated with juvenile idiopathic arthritis (68.2%), idiopathic uveitis (22.7%), and pars planitis (9.1%). The average age at surgery was 9.8±3.7 (5 to 17) years. With a mean follow-up of 33.6±18.3 (10 to 78) months, the overall surgical success was 81.8%. The cumulative survival probability after up to 2 trabeculotomies was 0.86 (95% confidence interval, 0.71-0.93) at 12 months and 0.77 (95% confidence interval, 0.60-0.87) at 24 months. Four (11.5%) eyes required a second trabeculotomy to achieve surgical success and 4 (7.7%) required filtrating procedures. Intraocular pressure improved from 31.4±7.6 (18 to 50) mm Hg preoperatively to 15.0±3.6 (8 to 23) mm Hg at final visits, whereas the number of glaucoma medications decreased from 4.2±1.1 (1 to 5) to 0.4±1.0 (0 to 4). Visual acuity and intraocular inflammation remained stable (P>0.05) and there were no major complications. CONCLUSIONS: Trabeculotomy is a safe and effective surgery for pediatric UG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".