Hydrus microstent compared to selective laser trabeculoplasty in primary open angle glaucoma: one year results
Bibliographic record
Abstract
BACKGROUND: To compare the reduction of intraocular pressure (IOP) and glaucoma medications following selective laser trabeculoplasty (SLT) versus stand-alone placement of the Hydrus microstent, a microinvasive glaucoma surgery device. DESIGN: Prospective interventional case-series. University practice. PARTICIPANTS: Fifty six eyes (56 patients) with uncontrolled primary open-angle glaucoma. METHODS: Patients received either SLT (n = 25) or Hydrus implantation (n = 31) in two centres. Patients were evaluated at baseline and 1, 7 days, 1, 3, 6 and 12 months after surgery. MAIN OUTCOME MEASURES: Intraocular pressure and number of glaucoma medications variations inter-groups and intra-groups. RESULTS: There were no significant differences at baseline between groups, but the mean deviation was worse in the Hydrus group (-8.43 ± 6.84 dB, confidence limits (CL)-2.8/-3.3 vs.-3.04 ± 0.65 dB, CL-6/-10.8; P < 0.001). After 12 months, there was a significant decrease in IOP and medications in the Hydrus group compared with baseline values. In the SLT group, only the decrease in IOP was significant. There was 3-fold greater reduction in medication use in the Hydrus group compared with SLT (-1.4 ± 0.97 vs.-0.5 ± 1.05, P = 0.001). 47% of patients were medication free at 12 months in the Hydrus group (4% in the SLT group). No complications were recorded in the SLT group. In the Hydrus group, three patients experienced a temporary reduction of visual acuity post-operatively, and two patients had post-operative IOP spikes that resolved within one week. CONCLUSIONS: Both SLT and Hydrus implantation reduced IOP without serious adverse events. Hydrus implantation led to a significant and further reduction in medication dependence at 12 months.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".