Development of a Prediction Rule to Estimate the Probability of Acceptable Intraocular Pressure Reduction After Selective Laser Trabeculoplasty in Open-angle Glaucoma and Ocular Hypertension
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Bibliographic record
Abstract
PURPOSE: To develop and validate a prediction rule to estimate the probability of acceptable intraocular pressure (IOP) reduction after selective laser trabeculoplasty (SLT) in ocular hypertension and open-angle glaucoma. PATIENTS AND METHODS: The study population was derived from a cohort of 220 patients with ocular hypertension, open-angle glaucoma, or normal tension glaucoma. A > or =20% reduction in IOP (mm Hg) from the baseline IOP at 6 months after SLT was considered treatment success. Logistic multivariate regression modeling was performed to develop a prediction rule. RESULTS: In multivariate logistic regression analyses, pre-SLT IOP and maximum IOP were identified as independent predictors for > or =20% IOP reduction at 6 months with adjusted odds ratios of 1.3 and 0.9, respectively, controlling for sex, diagnosis, pigment of anterior chamber, and washout of eye drops. The area under receiver operator characteristic curve was 0.716. Calibration of this prediction rule showed good agreement between predicted and observed probabilities of acceptable IOP reduction. If a probability of acceptable IOP reduction of 50% or greater is used as the minimal clinical threshold for treatment, the prediction rule had a sensitivity and specificity of 91.3% and 30.4%, respectively. CONCLUSIONS: SLT efficacy is positively associated with IOP elevation before SLT treatment and adversely associated with the maximum IOP ever recorded in history. Pigmentation of the anterior chamber angle, diagnosis, washout of eye drops, and sex are not associated with SLT treatment efficacy. This prediction rule should be further validated with a comparable prospective clinical study cohort.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it