Episcleral Venous Outflow: A Potential Outcome Marker for iStent Surgery
Bibliographic record
Abstract
PURPOSE: (1) To propose the use of episcleral venous outflow (EVO) as an outcome marker of iStent surgery, and an EVO grading scale. (2) To determine the association of EVO with: (a) postoperative intraocular pressure (IOP) and medication burden; (b) iStent patency status. PATIENTS AND METHODS: Retrospective cohort study including 151 glaucomatous eyes having undergone iStent-phacoemulsification surgery. Demographic and preoperative data (IOP, number of antiglaucoma medications, glaucoma type and stage, maximal IOP) were collected. Postoperatively, were recorded: IOP, number of antiglaucoma medications, occurrence of stent malpositioning or obstruction, and EVO scores based on the proposed scale (0: no laminar flow; 1+: faint laminar flow; 2+: marked laminar flow). A Kruskal-Wallis test determined the association between EVO, postoperative IOP, and medication burden. A multivariable-adjusted ordinal logistic regression was used for the association with iStent patency status. RESULTS: Patients with marked laminar flow (2+) were more likely to have a lower IOP (P=0.022) and fewer medications (P=0.009) at 1-year postoperatively than those with no laminar flow (0). No difference was found in postoperative IOP and number of medications when comparing patients having faint laminar flow (1+) with patients from the other 2 EVO categories (0 and 2+). iStent patency was associated with greater EVO as opposed to its obstruction (odds ratio, 4.73; 95% confidence interval, 1.74-12.9). No malpositioned stents were noted in our cohort. CONCLUSIONS: The use of EVO as an outcome marker of iStent surgery is physiologically plausible. The proposed EVO grading scale is simple, comprises few categories, and is easily applicable in an in-office setting. The results of this study suggest this scale could be useful in the assessment of iStent functionality and encourage its further investigation in prospective studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".