Novel open-source 3D-printed eye mount (TEMPO) for the ophthalmology wet lab
Bibliographic record
Abstract
OBJECTIVE: Procuring an affordable eye mount that can stabilise a cadaveric eye and simulate a patient's normal facial contours represents an ongoing challenge in the ophthalmology simulation wet lab, with notable limitations to all currently available commercial options. This project uses computer-assisted design and three-dimensional (3D)-printing techniques to tackle these challenges for ophthalmologic surgical training. METHODS AND ANALYSIS: Proof-of-concept study. Using Autodesk Fusion 360, we designed and 3D-printed a modular device that consists of 11 pieces forming a head structure. Standard OR tubing and syringes were adapted to create an adjustable-suction system to affix cadaveric eyes. Further modular inserts were customised to house non-cadaveric simulation eyes. RESULTS: Three-dimensional eye mount for procedures in ophthalmology (TEMPO) reliably fixed a cadaveric eye in stable position throughout surgical manipulation. Trainees were able to drape and practice appropriate hand positioning while corneal suturing. Overall, this model was affordable, at a cost of approximately $C200 to print. The modular nature renders individual pieces convenient for replacement and customisable to simulate regional anatomical variation and accommodate non-cadaveric eyes. CONCLUSIONS: TEMPO represents an affordable, high-fidelity alternative to existing commercially available eye mounts. It reliably fixates cadaveric and simulation eyes and provides an enhanced surgical training experience by way of its realistic facial contours. It is released as an open-source computer-aided design file, customisable to interested trainees with appropriate software and 3D-printing capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".