Cadaver corneoscleral model for angle surgery training
Bibliographic record
Abstract
PURPOSE: To assess a new training model (Tackdriver) for new-generation microinvasive glaucoma surgeries (MIGS). SETTING: Resident training centers in Canada. DESIGN: Evaluation of technology. METHODS: Human cadaver corneoscleral rims recovered after Descemet-stripping endothelial keratoplasty or not suitable for transplantation were acquired from an eye bank. The tissue was fixated with a single tack through the center of the cornea, which was inverted in a concave fashion. A water-based medical lubricant was used for placement of a goniolens after visualization of the anterior chamber and the trabecular meshwork. Microbypass stent (iStent) insertion and gonioscopy-assisted transluminal trabeculotomy (GATT) were practiced on this model. The model was qualitatively assessed for ease of preparation, accuracy of surgical simulation, and the number and variety of MIGS procedures that can be performed. RESULTS: Efficient high-volume training was performed for microbypass stent insertion using first-generation and second-generation microbypass stents. The GATT procedure was also performed as a final step in a titratable fashion in 90-, 180-, or 270-degree segments or a complete 360-degree treatment. The model simulated bimanual angle surgery with good fidelity. CONCLUSIONS: The training model allowed for high-volume bimanual MIGS training for techniques such as microbypass stent insertion and removal as well as GATT. Preparation was relatively simple, efficient, and cost-effective compared with other models. Inverting the specimen allowed the trainee to practice MIGS techniques independent of the tissue's corneal clarity. Other MIGS techniques and angle training procedures can be adopted to this model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".