A Novel 8-mm Schlemm's Canal Scaffold Reduces Outflow Resistance in a Human Anterior Segment Perfusion Model
Bibliographic record
Abstract
PURPOSE: To study the effect on outflow facility and outflow resistance of a nitinol microstent implanted into Schlemm's canal. METHODS: Using a constant pressure perfusion method, outflow facility and outflow resistance were measured in 26 pairs of dissected anterior segments from donated human eyes. Measurements were made at perfusion pressures of 10, 20, 30 and 40 mm Hg. The Hydrus Microstent was placed in Schlemm's canal of one eye and the contralateral eye underwent a sham procedure. Outflow facility and outflow resistance were measured again after the microstent implantation or sham procedure. RESULTS: The Hydrus Microstent significantly increased outflow facility from 0.33 ± 0.17 μL/min/mm Hg to 0.52 ± 0.19 μL/min/mm Hg (P < 0.001). Outflow resistance was significantly reduced from 4.38 ± 3.03 mm Hg/μL/min at baseline to 2.34 ± 1.04 mm Hg/μL/min (P < 0.001) with the microstent. There was a linear correlation between outflow resistance at baseline and decrease in outflow resistance with the microstent (R(2) = 0.89, P < 0.0001). CONCLUSIONS: The increase in outflow facility and decrease in resistance supports the potential use of the Hydrus Microstent as a surgical option to reduce intraocular pressure (IOP). The IOP-lowering effect may be higher in eyes with higher outflow resistance (and IOP) as compared with eyes with lower outflow resistance (and IOP).
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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".