Long-term outcomes in Primary congenital glaucoma, aniridia and anterior segment dysgenesis
Bibliographic record
Abstract
Aim To determine the long-term outcomes of a cohort of complex patients with primary congenital glaucoma, aniridia and anterior segment dysgenesis. Methods Retrospective consecutive series between 1990–2021 in two UK tertiary centres: Guy's and St Thomas’ NHS Foundation Trust and King's College Hospital NHS Foundation Trust. We recorded the number and types of surgical and laser treatments along with preoperative and postoperative data, including intraocular pressures (IOP) and anti-glaucoma medications. Results A total of 41 eyes of 21 patients were included. Primary diagnoses were primary congenital glaucoma in 16 eyes (39.0%), aniridia in 14 eyes (34.2%), and anterior segment dysgenesis in 8 eyes (19.5%). Sixteen eyes (39.0%) had one or more glaucoma surgery or laser procedures for advanced glaucoma, and the long-term follow-up was 12.8 ± 3.6 years. There was a significant decrease in postoperative IOP (mmHg) at 3 months (16.5 ± 1.6; p = 0.0067), 6 months (18.7 ± 2.1; p = 0.0386), 12 months (18.6 ± 1.7; p = 0.0229), 3 years (14.7 ± 1.2; p = 0.0126), 5 years (15.5 ± 1.8; p = 0.0330) and 10 years (15.4 ± 2.3; p = 0.7780), compared to preoperatively (24.1 ± 2.6). Surgical success (complete and qualified) was 62.5%, 50.0%, 43.8%, 46.2%, 45.5% and 28.6% at 3 months, 6 months, 12 months, 3 years, 5 years and 10 years, respectively. There was no significant change in the number of anti-glaucoma drugs postoperatively ( p > 0.05). Four eyes (25.0%) had postoperative complications (hyphaema, hypotony) that resolved after conservative management. Conclusions Surgical management of these complex eyes with advanced glaucoma is challenging. Overall, the cohort had good surgical outcomes with a significant decrease in IOP by 36.1% after long-term follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".