Collagen Architecture of the Posterior Pole: High-Resolution Wide Field of View Visualization and Analysis Using Polarized Light Microscopy
Bibliographic record
Abstract
Purpose: The purpose of this study was to leverage polarized light microscopy (PLM) to visualize the collagen fiber architecture of posterior pole and optic nerve head with micrometer-scale resolution and to identify and quantify major organizational components. Methods: Eight sheep posterior poles were cryosectioned and imaged using PLM. Collagen fiber orientation was determined by using custom scripts, and the resulting orientation maps were inspected and quantified to identify major structural elements and tested for differences in mean fiber orientation and anisotropy, using linear mixed effect models. Results: Images revealed an intricate organization of collagen fibers in the posterior pole. In the lamina cribrosa, interweaving fibers formed large knots and wrapped around nerve fiber pores, with beam insertions into the scleral canal wall that were either narrow and straight or wide. In the peripapillary sclera, three significantly different (P < 0.0001) components were identified: fibers oriented circumferentially proximal to the canal, radially in the innermost sclera, and unaligned with interweaving fibers. The radial fibers were between 60 and 180 μm thick, extending at least 3 mm from the canal. Conclusions: PLM revealed structural aspects of the lamina cribrosa and sclera that may have important biomechanical roles but that were previously unreported or not characterized quantitatively. In the lamina cribrosa, these roles included wide and narrow beam insertions and details of collagen fibers interweaving and wrapping around the pores. In the sclera, we described regions of circumferential, radial, and unaligned "random" fibers. Although there is consensus that circumferential fibers protect neural tissues by resisting canal expansion, the role of the radial fibers remains unclear.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".