Postoperative aqueous outflow in the human eye after glaucoma filtration surgery: biofluidmechanical considerations
Bibliographic record
Abstract
To evaluate the possible postoperative outflow from the anterior chamber of the eye after filtration surgery, a mathematical model based on fluid mechanical principles and clinical data is proposed. Two ophthalmic surgical procedures, non-penetrating deep sclerectomy and trabeculectomy, were analyzed. Based on mathematical modeling, the amount of postoperative outflow through the fistula (after trabeculectomy) or membrane (after non-penetrating surgery) as well as the outflow through residual natural drainage pathways were calculated and compared. From our model, the following results were obtained: 1) if trabeculectomy is carried out in an eye with preoperative intraocular pressure (IOP) of 30 mm Hg and postoperative IOP=10 mm Hg, only 10% of aqueous utilizes the natural outflow pathway via the trabecular meshwork, whereas if non-penetrating surgery is carried out in the same eye with postoperative IOP=16 mm Hg, the outflow through the trabecular meshwork amounts to 35%. Thus, non-penetrating surgery provides more aqueous outflow along the natural outflow pathways than trabeculectomy. 2) Generally, the higher the postoperative IOP and/or the lower the preoperative IOP, the higher the amount of aqueous, which will utilize the natural outflow pathways postoperatively. 3) The reestablishment of aqueous production postoperatively in addition to other factors, such as wound healing, may be a reason for IOP increase during the postoperative period.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".