Optical Coherence Tomography for Efficient Diagnosis of Ophthalmological Optical Pathway Disease using Machine Learning
Bibliographic record
Abstract
The development of modern science and technology are more and more reflect the intersection and penetration of multiple disciplines. A new three-dimensional digital image modeling system integrating medical science, biomechanics, mechanics, materials science, computer graphics, mathematical analysis, automatic control and other multidisciplinary fields is currently in rapid development. This has led to great changes in the methods and concepts of modern medical diagnosis and treatment. In recent years, with the development of CT, MRI and other image diagnostic instruments, the application of computer virtual reality technology in medicine has been rapidly developed. Computers use this image information to model three-dimensional data images, providing surgeons with objective, accurate, intuitive, and scientific methods for surgical simulation, surgical navigation, surgical positioning, and surgical planning .At the same time, it also provides objective and accurate reference value for imaging diagnosis and interventional treatment.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".