Histological Assessment of Foveal Mechanical Instability and Potential Implications for Macular Diseases
Bibliographic record
Abstract
Purpose: To assess areas of mechanical instability in the fovea based on a review of artifactual separations of histological sections. Methods: A collection of annotated high-resolution retinal histological sections of aged donors was assessed for tissue disruptions in the fovea. Sections belong to Project MACULA (https://projectmacula.org). Results: Sections (mean length, 7467 ± 491 µm) through 75 foveas were analyzed (61% [46/75] from females; mean age at death, 81.3 years). Of these, 68% (51/75) were normal-aged, and 32% (24/75) showed early non-neovascular AMD. Separation of the neurosensory retina from the RPE occurred in 96% of sections (72/75). A break between the photoreceptor inner and outer segments was observed in 57% of cases (41/72), and bacillary layer detachment was observed in 15% (11/72), primarily involving the subfoveal region. Henle fiber layer disruptions were observed in 64% of sections (48/75), being around the Müller cell cone (MCC) in 90% of cases (43/48) and extending to cone photoreceptor cell bodies in 58% (28/48). Cystic changes at the MCC occurred in 50% of sections (38/75), with partial or complete disinsertion of the MCC in 47% of these (18/38). Conclusions: Histological sections from normal-aged and early AMD eyes exhibit frequent tissue disruptions from post mortem handling, corresponding with regions of separation observed on OCT scans of various vitreoretinal diseases. Findings suggest that foveal areas of low mechanical stability may manifest as common ex vivo artifactual disruptions. Observing how foveal tissue breaks down post mortem may provide greater insight into the pathophysiology of different vitreoretinal diseases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".