Quantitative Study of the Macular Microvasculature in Human Donor Eyes
Bibliographic record
Abstract
Purpose: To precisely quantify the macular microvasculature density using microperfusion and labeling techniques in human donor eyes. Such information may be useful in understanding the role of the macular microvasculature in coping with the metabolic requirements of the neurons in this densely packed region, and provide a reference point for clinical studies using recently developed optical imaging techniques. Methods: The macular microvasculature was perfusion-labeled in 18 human donor eyes and optical stacks collected from regions superior, temporal, inferior, and nasal to the foveola using confocal microscopy. The optical slices were separated into the deep macula vascular layer (DL), and the superficial layer (SL) in which all the vessels superficial to the deep macular vessel layer were included. The DL and SL images were analyzed and vessel density measured according to their orientation from the foveola and in foveal and parafoveal regions. Vessel densities were compared across regions and age groups. Results: Both the SL and DL showed an increase in vessel density with increasing eccentricity from the foveal to parafoveal regions. Vessel density was found to rank in the order of inferior > superior > temporal > nasal in both SL and DL layers. The SL vascular density was approximately 31%, whereas DL was approximately 17%. The DL was planar in nature and density not affected by age. Age-related increase in vessel density was observed in the SL. Conclusions: Microperfusion and labeling techniques in combination with confocal microscopy has enabled collection of reliable data on vascular density in the macula region. Regional differences may reflect well-matched vascular supply and neuronal demands. Age-related changes might indicate the importance of stable blood supply for the human macula.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".