K-RIM (Corneal Rim) Angle Surgery Training Model
Bibliographic record
Abstract
PURPOSE: To develop an angle surgery training model for training an array of microincisional glaucoma surgery (MIGS) procedures. METHODS: We describe a method for preparing an angle surgery training model using human cadaveric corneoscleral rims. The model provides realistic tactile tissue simulation and excellent angle visualization requiring bimanual technique. Corneoscleral rims may be used multiple times and are prepared at low cost, allowing for a high volume of practice surgeries. RESULTS: This model allows for practice in bimanual surgical training using the gonioscopy lens for visualize alongside surgical tools. The in vivo surgical conditions and limited tactile feedback are recreated using human cadaveric eyes which nonhuman models fail to provide. Our model is prepared at low cost, with relative ease and also provides appropriate positioning of Schlemm canal and for high volume of practice as the canal can be used in 90-degree segments. CONCLUSIONS: Few angle surgery training models currently exist and none provide these necessary features. The model presented here aims to meet the growing demand for adequate training models required for technically advanced MIGS techniques.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".