Bibliographic record
Abstract
PURPOSE: To investigate if specific classes of antiglaucoma medications have an influence on selective laser trabeculoplasty (SLT) success. METHODS: This retrospective prediction rule analysis investigated 120 eyes from 120 patients diagnosed with either open angle glaucoma or ocular hypertension, who underwent SLT treatment. Treatment success was defined as ≥20% intraocular pressure (IOP) reduction at 3 and 6 months after the treatment date. Multivariate logistic regression analyses were performed to determine success predictors. RESULTS: Pre-SLT IOP (up to 4 wk before SLT therapy) was the only independent predictor for ≥20% IOP reduction with an odds ratio of 1.30 when controlling for pre-SLT antiglaucoma drops. The area under receiver operator characteristic curve was 0.777. CONCLUSIONS: Topical medications do not adversely, nor favorably, affect SLT success. SLT efficacy is positively associated with the degree of IOP elevation before SLT treatment. Pigmentation of the anterior chamber angle, class of antiglaucoma medications, diabetes, sex, corneal thickness, pseudophakia, diagnosis, washout of eye drops, and previous argon laser trabeculoplasty treatment are not associated with SLT treatment efficacy.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".