3D-Printed eye phantoms as physiological and pathological standards for training and instrument development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early identification of conditions that can lead to blindness is critical for saving vision. The optical red-reflex test (RRT), which assesses the light reflections from the back of the eye, is a key exam for identifying adverse eye conditions in very young children. However, healthcare workers generally learn the RRT using peer practice and do not have the opportunity to observe abnormal reflexes, especially for rarer conditions, during training. The light reflections also differ in appearance between populations due to different pigmentation levels, so effective training requires practice with a diverse population. We have developed a set of 3D model eyes that aim to accurately mimic the response of eyes with varying pigmentation levels in the RRT, both for healthy eyes and pathologies that can be identified using the RRT. We characterized the optical properties of a set of full-color 3D printing materials (a white scattering material and four transparent colors - cyan, magenta, yellow and black). These properties were used to determine the number of layers, layer thicknesses, and color and scattering material combinations needed to match the reflectance of different fundi, given the constraints of the 3Dprinter. The model eyes can be used as an inexpensive tool for training a wide variety of health professionals to recognize abnormal reflections from the eye and as a reference standard for developing or calibrating eye screening instruments and tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it