Three-Dimensional Printing of a Transconjunctival Vitrectomy Trocar-Cannula System
Bibliographic record
Abstract
PURPOSE: The aim of this paper was to study the feasibility of manufacturing a customizable trocar-cannula system for vitreoretinal surgery utilizing commercially available three-dimensional (3D) printing technology. METHODS: A digital model of a trocar-cannula system for vitreoretinal surgery was created using computer-aided design (CAD) software and printed utilizing a laser-sintering 3D printer in modified ABS thermoplastic material. The trocar-cannula prototypes were tested in pig eyes. RESULTS: A customizable digital model was created using commercially available CAD software. Three trocar-cannulas were printed. The smallest cannulas that could be printed had dimensions between 21 and 22G. The trocar-cannulas were inserted in pig eyes after performing sclerotomies with a commercially available 20G MVR blade. One cannula broke during insertion. CONCLUSIONS: This study demonstrates the feasibility of printing a transconjunctival vitrectomy trocar-cannula system with commercially available 3D print technology. The 3D printer and build material used resulted in trocar-cannulas with functional limitations including a minimum size achievable and mechanical resistance.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".