Outcomes and Predictors of Failure of Ultrasound Cyclo Plasty for Primary Open-Angle Glaucoma
Bibliographic record
Abstract
Aims: To evaluate the outcomes of ultrasound cyclo plasty (UCP) for primary open-angle glaucoma (POAG) and identify the predictors of failure. Methods: This retrospective cohort study included patients with POAG who underwent UCP at King Abdul Aziz University Hospital, Riyadh, Saudi Arabia, between 2016 and 2021. The main outcome measures were the intraocular pressure (IOP), the number of antiglaucoma medications, and the presence of vision-threatening complications. The surgical outcome of each eye was based on the main outcome measures. Cox proportional hazard regression analysis was performed to identify the possible predictors of UCP failure. Results: Sixty-six eyes of fifty-five patients were included herein. The mean follow-up period was 28.95 (±16.9) months. The mean IOP decreased significantly from 23.02 (±6.1) to 18.22 (±7.0) and 16.44 (±5.3) mm Hg on the 12th and 24th months, respectively; the mean number of antiglaucoma medications decreased significantly from 3.23 (±0.9) to 2.15 (±1.5) and 2.09 (±1.6), respectively. The cumulative probabilities of overall success were 71.2 ± 5.6% and 40.9 ± 6.1% on the 12th and 24th months, respectively. High baseline IOP and the number of antiglaucoma medications were associated with a higher risk of failure (hazard ratio = 1.10 and 3.01, p = 0.04 and p < 0.01, respectively). The most common complications were cataract development or progression (30.8%) and prolonged or rebound anterior chamber reaction (10.6%). Conclusions: UCP reasonably controls the IOP and reduces the antiglaucoma medication burden in eyes with POAG. Nevertheless, the success rate is modest, with a high baseline IOP and number of medications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".