Reticular patterned episcleral venous plexus and 360-degree episcleral venous fluid wave after hemi-gonioscopy assisted transluminal trabeculotomy
Bibliographic record
Abstract
We describe a method for eliciting an episcleral venous fluid wave (EVFW) in eyes presenting with reticular patterned episcleral venous plexus, after a hemi-gonioscopy assisted transluminal trabeculotomy (hemi-GATT). To reduce the risk of post-operative hyphema and reduce intraoperative tissue manipulation, a hemi-GATT (targeting 180-degrees of Schlemm’s canal) was performed. Post-hemi-GATT, the ability to inject balanced salt solution and obtain an EVFW in both the treated (inferior) and untreated (superior) sectors of the eye supports the surgical success of the technique, and demonstrates an enhanced fluid outflow and subsequent vessel blanching. The pre-operative intraocular pressure of 20/21 mmHg in a single subject decreased to 18-, 12- and 15-mmHg after one day, one month and 3 months post-op, respectively, and the subject was rendered medication-free. This method of performing a hemi-GATT to effectively obtain an EVFW provides evidence for novel treatment algorithms in patients with a reticular episcleral venous plexus where identification of major outflow vessels is less apparent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".