Intraoperative predictors of success of iStent placement with cataract surgery
Bibliographic record
Abstract
OBJECTIVE: To investigate and assess the intraoperative predictors for successful 1-year outcomes of iStent inject for patients with open angle glaucoma. DESIGN: Retrospective case series. PARTICIPANTS: Patients who underwent combined iStent inject placement and cataract surgery between October 2018 and August 2022. METHODS: A priori predictors of interest included the number of stents placed, the number of clicks required to place them, stent spacing, intraoperative reflux of blood from the stent, and observed flow of aqueous through external vasculature. The primary outcome was the intraocular pressure (IOP) medication index. RESULTS: This study included 99 eyes of 57 patients. The mean preoperative IOP was 14.9 (±3.7) mm Hg, and mean number of drops were 1.7 (±0.7). The mean postoperative follow-up was 15.6 (±5.9) months. The mean postoperative IOP was 13.6 (±6.1) mm Hg and medication reduction was -1.5 (±4.2 mm Hg). Using the IOP medication index, 92.6% of eyes were categorized as having a successful procedure. Multivariate analysis showed that flow in both one (p = 0.030) and two (p = 0.034) stents were independent predictors of success after 1 year. CONCLUSIONS: The IOP medication index showed a statistically significant association between flow from both 1 and 2 stents. Flow can be used as a predictor by surgeons to change patients' surgical plans intraoperatively and to monitor patients more closely postoperatively.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".