Baseline IOP predicts selective laser trabeculoplasty success at 1 year post-treatment: results from a randomised clinical trial
Bibliographic record
Abstract
BACKGROUND/AIMS: The efficacy and safety of selective laser trabeculoplasty (SLT) has been found to be equivalent to argon laser trabeculoplasty (ALT). Since SLT produces significantly less disturbance to the trabecular meshwork and is theoretically more repeatable than ALT, it has potential to replace ALT as the standard procedure to treat medically uncontrolled open angle glaucoma. This study's objective is to determine factors that predict successful SLT at 1 year post-treatment. METHODS: As part of a randomised clinical trial comparing the efficacy and safety of SLT to ALT, data on 72 SLT patients were collected, and successful SLT defined as having an SLT induced intraocular pressure (IOP) reduction of >or=20% at 1 year post-treatment follow up. RESULTS: 43 out of the 72 patients who had completed their 1 year follow up visit had an IOP reduction of >or=20% from baseline. No glaucoma risk factors studied predicted successful SLT. The amount of trabecular meshwork pigmentation was not a significant predictor. However, it was discovered that baseline IOP strongly predicted SLT success (odds ratio=1.16; p=0.0001). CONCLUSION: SLT success was significantly predicted by baseline IOP but not by age, sex, other glaucoma risk factors, type of open angle glaucoma, or by degree of trabecular meshwork pigmentation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".