A Novel Schlemm's Canal Scaffold Increases Outflow Facility in a Human Anterior Segment Perfusion Model
Bibliographic record
Abstract
PURPOSE: An intracanalicular scaffold (Hydrus microstent) designed to reduce intraocular pressure as a glaucoma treatment was tested in human anterior segments to determine changes in outflow facility (C). METHODS: Human eyes with no history of ocular disease or surgeries were perfused within 49 hours of death. The anterior segments were isolated and connected to a perfusion system. Flow rates were measured at pressures of 10, 20, 30, and 40 mm Hg. The scaffold was inserted into Schlemm's canal of the experimental eye, while a control eye underwent a sham procedure. Flow rate measurements were repeated at the four pressure levels. Individual C values were computed by dividing the flow rate by its corresponding pressure, and by averaging the four individual C measurements. The change in C between control and experimental eyes was assessed by the ratio of the baseline and second C measurement. In two eyes, the placement of the scaffold was evaluated histologically. RESULTS: After scaffold implantation in the experimental eyes, the average C increased significantly from baseline (n = 9, P < 0.05). Ratios of C at all pressure levels, except for 10 mm Hg, were significantly higher in experimental eyes (n = 9) than control eyes (P < 0.05, n = 7). Histologically, the scaffold dilated Schlemm's canal with no visible damage to the trabecular meshwork. CONCLUSIONS: The Hydrus Microstent provided an effective way to increase outflow facility in human eyes ex vivo.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".