<em>In Vivo</em> Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
Bibliographic record
Abstract
Advancements in ophthalmic imaging tools offer an unprecedented level of access to researchers working with animal models of neurovascular injury. To properly leverage this greater translatability, there is a need to devise reproducible methods of drawing quantitative data from these images. Optical coherence tomography (OCT) imaging can resolve retinal histology at micrometer resolution and reveal functional differences in vascular blood flow. Here, we delineate noninvasive vascular readouts that we use to characterize pathological damage post vascular insult in an optimized mouse model of retinal vein occlusion (RVO). These readouts include live imaging analysis of retinal morphology, disorganization of retinal inner layers (DRIL) measure of capillary ischemia, and fluorescein angiography measures of retinal edema and vascular density. These techniques correspond directly to those used to examine patients with retinal disease in the clinic. Standardizing these methods enables direct and reproducible comparison of animal models with clinical phenotypes of ophthalmic disease, increasing the translational power of vascular injury models.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".