Numerical Simulation of Aqueous Flow in Laser Iridotomy
Bibliographic record
Abstract
Primary angle-closure glaucoma (PACG) is a major cause of blindness worldwide, with a particularly high prevalence in Asian populations.Laser iridotomy (LI) has been the standard therapeutic modality for the treatment of PACG to avoid blindness.However, physiological mechanism of LI has not been fully understood, due to the complex structure of the eyeball, the fluidity of aqueous, and the limitation of detecting equipment.It will increase the difficulty of surgery and the probability of complications.Based on the above reason, numerical analysis was conducted to investigate the aqueous humor flow under different physiological structures before and after laser surgery.It will be helpful to explore the pathological cause of primary angle-closure glaucoma, prevent the complications of laser iridotomy, and provide new ideas for clinical guidance.In this paper, the main flow domain of aqueous humor was modelled by three kinds of structure of eyeball according to the physiological theory of the treatment process.They were normal eyeball, eyeball with the physiological structure of shallow anterior chamber depth and narrow angle and eyeball after laser iridotomy treatment.The finite volume method was used to discretize the computational model area.The flow of aqueous humor is simulated.The results showed that, 1) the geometric model of physiological structure of shallow anterior chamber depth was very important for theoretical study of glaucoma.When the depth of anterior chamber decreased from 2.8 mm to 2.0 mm, the maximum velocity of natural convection doubled, and the pressure difference of shallow anterior chamber increased by 20%.2)Pupil block increases intraocular pressure sharply, which is equivalent to thousands of times the normal pressure, causing damage to intraocular tissues.3)Laser iridectomy can effectively reduce the intraocular pressure caused by pupil block, but the velocity of aqueous humor after operation is 40 times of normal speed, and the increase of corneal shear stress leads to corneal damage.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".