Sequence and Detectability of Changes in Macular Ganglion Cell Layer Thickness and Perfusion Density in Early Glaucoma
Bibliographic record
Abstract
Purpose: To investigate whether macular perfusion density (PD) changes measured over time by optical coherence tomography angiography (OCTA) are detectable before progressive macular ganglion cell layer (GCL) thinning in early glaucoma. Methods: This prospective longitudinal cohort study involved patients with early open-angle glaucoma and healthy subjects imaged by OCT and OCTA every 4 months. GCL thickness and macular PD were evaluated in 16 tiles in the macula. We estimated baseline percentage losses of GCL thickness or macular PD in glaucoma patients with age-corrected normative values derived from the healthy subjects. Additionally, the threshold slope separating glaucoma patients from healthy subjects with 90% specificity was used to determine the number of patients with steeper slopes than the threshold slope. Results: Eighty patients with glaucoma and 42 healthy subjects were included. In eight tiles (50%), patients with a significant macular PD slope had a significantly greater baseline percentage loss of GCL thickness relative to macular PD compared to patients without a significant macular PD slope. Furthermore, in 15 tiles (94%), a greater baseline percentage loss of GCL thickness relative to PD was significantly correlated with faster PD slopes. In contrast, only one tile (6%) showed these significant trends for GCL slopes. The number of patients with faster GCL slopes than threshold slopes was significantly larger than patients with faster PD slopes in 12 tiles (75%). Conclusions: A decrease in GCL thickness precedes a measurable decrease in macular PD. Early glaucomatous progression is more frequently detectable with changes in GCL thickness compared to macular PD.
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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 | 0.002 |
| 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) | 0.001 | 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".