Bibliographic record
Abstract
Diabetic retinopathy (DR) is the most common complication of diabetes and the leading cause of vision loss worldwide. Clinical detection relies on the manifestation of sight-threatening microvascular, macrovascular and edematous ocular insults. However, preclinical investigations detected retinal thickness irregularities. We hypothesized that retinal thickness irregularities are localized to specific retinal regions and layers. Training and validation data were collected from participants with diabetes with no/minimal DR and healthy individuals to identify and verify regions of interest. Optical coherence tomography and MATLAB速 computing were the primary tools used to measure retinal thickness in each participant. Linear mixed-effects models identified four significant regions of thickness irregularities from the training data; these were not matched by validation data. Nonetheless, retinal thickness irregularities were localized to specific regions and layers. Recognizing that there are retinal regions susceptible to retinal thickness irregularities during preclinical stages of disease is important for early disease detection.
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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 | 0.000 | 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) | 0.005 | 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".