Phacoemulsification and Goniosynechialysis in the Management of Unresponsive Primary Angle Closure
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness of phacoemulsification and goniosynechialysis (PEGS) in managing acute and subacute primary angle closure unresponsive to conventional therapy. MATERIALS AND METHODS: Retrospective series of patients of six glaucoma-trained surgeons with primary angle closure that did not respond to medical management, Nd:YAG laser peripheral iridotomy, or argon laser peripheral iridoplasty. RESULTS: A total of twenty-one patients with an average age of 65.6 years were included. Underlying mechanism of angle closure included pupillary block (n = 18) and plateau iris (n = 3). Average intraocular pressure (IOP) immediately prior to PEGS was 40.7 mm Hg, and mean follow-up time after PEGS was 11.7 months. PEGS decreased mean IOP by 25 mm Hg (62%), and mean number of medications from 3.8 pre-surgery to 1.7 post-surgery (55%). Mean LogMar visual acuity improved after PEGS, from 0.64 to 0.44 (Paired t test t = 4.120 P = 0.001). Subsequent trabeculectomy was necessary in one case (5%). CONCLUSIONS: Phacoemulsification with goniosynechialysis may be an effective treatment option for primary angle closure unresponsive to conventional therapy.
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 | 0.003 |
| 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".