Efficacy and Safety of Kahook Dual Blade Goniotomy and Trabecular Micro-Bypass Stent in Combination with Cataract Extraction
Bibliographic record
Abstract
In recent years, rapid advancements in glaucoma research have led to the development of more effective treatments of this chronic and irreversible condition. Of these, Kahook Blade Dual (KDB) goniotomy and second-generation trabecular micro-bypass stent (iStent) are two novel biomimetic procedures which have designs inspired by the eye’s natural drainage mechanisms. In this retrospective study, we evaluated the safety and effectiveness of both surgeries by including 176 eyes from 110 patients: 142 eyes in the iStent group and 34 in the KDB group. The primary outcomes of this study were the proportions of patients in each group attaining a 20% reduction in IOP and a post-operative IOP < 19 mmHg. At the last follow-up, a 20% reduction in IOP was achieved by 67% of iStent inject patients and 50% of KDB patients (p = 0.07). The iStent group also showed a higher proportion of patients reaching an IOP of less than 19 mmHg (81% vs. 71% in the KDB group, p = 0.13). The number of medications did not decrease in either group from pre-op to the last follow-up. The KDB group had more failures (29.4% vs. 4.2%) and a significantly higher adverse event rate than the iStent inject group (47.1% vs 12.0%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".