Mathematical Modeling of Outflow Facility Increase With Trabecular Meshwork Bypass and Schlemm Canal Dilation
Bibliographic record
Abstract
PURPOSE: To mathematically model the conventional aqueous humor outflow system with trabecular meshwork (TM) bypass and Schlemm canal (SC) dilation. METHODS: The SC was modeled as a rectangular channel with the TM modeled as a permeable membrane. The collector channels (CCs) were modeled as fluid sinks distributed along the outer wall of SC. Two different implants were investigated in this study. The Hydrus Microstent (scaffold) was modeled with a TM bypass and a dilated region in SC that was 7 or 15 mm long and approximately 5-fold larger than the normal height of SC (h0). The iStent trabecular microbypass was modeled with a similar structure except that the dilated region in SC was 1 mm long and 25% larger than h0. RESULTS: Creation of a TM bypass structure would increase the pressure in the surrounding regions inside the SC and make it close to the intraocular pressure. SC dilation would increase the pressure more uniformly in the dilated region. The pressure increase led to higher flow rates in SC and CCs, and subsequently increased outflow facility (C). If CCs were uniformly distributed, the increase in C was the smallest after implantation of 1 microbypass, compared with that after implantation of 2 microbypasses or 1 scaffold. If CCs were nonuniformly distributed, the magnitude of increase in C was sensitive to the location of implant, and the sensitivity was higher for the microbypass than the scaffold. CONCLUSION: The study showed that creation of TM bypass and SC dilation significantly increased outflow facility, and the amount of increase correlated with the length of dilated regions in SC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".