Predictors of selective laser trabeculoplasty success in open angle glaucoma or ocular hypertension: does baseline tonography have a predictive role?
Bibliographic record
Abstract
Background The determinants of success of selective laser trabeculoplasty (SLT) in treatment-naïve patients with open angle glaucoma (OAG) and ocular hypertension (OHT) have not been understood fully. Therefore, we have conducted this study to explore the predictors of success. Methods This is a retrospective review of a pre-existing database of patients who had received primary SLT at St Thomas’ Hospital, London, UK. Patients with OAG and OHT who had received primary 360° SLT treatment and had reliable baseline tonographic outflow facility (TOF) with minimum of 1 year of follow-up were included. Univariate and multivariate analyses were performed to find the determinants of success. Results One hundred and seventy-four patients between August 2006 and February 2010 had received primary 360° SLT treatment and had baseline TOF measurement. Of these, 72 subjects fulfilled the eligibility criteria. In multivariate regression analysis, the only variable associated with success was baseline intraocular pressure (IOP) (R 2 =0.32, beta=−0.51, p<0.001, 95% CI −2.02 to −0.74). Conclusion To our knowledge, this is the only study investigating the pretreatment TOF (measured with electronic Shiøtz tonography) and IOP as determinants of success 12 month’s post-360° SLT in treatment-naïve patients with OAG and OHT. This study demonstrated that pretreatment IOP (and not TOF) is the only determinant of success after primary SLT therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".