Understanding Patterns of Preserved Retinal Ganglion Cell Layer in Advanced Glaucoma as Seen With Optical Coherence Tomography
Bibliographic record
Abstract
PRÉCIS: Using optical coherence tomography (OCT), eyes with advanced glaucoma were found to have a wide range of patterns of damage that were consistent with the natural history of progression based on a model of macular progression. PURPOSE: To understand the patterns of preserved retinal ganglion cells in eyes with advanced glaucoma using OCT and a model of progression of the central macula. METHODS: OCT GCL thickness was measured in 94 eyes with advanced glaucoma, defined as glaucomatous eyes with a 24-2 MD (mean deviation) worse than -12 dB. A commercial report supplied the GCL thickness in 6 sectors of the thick, donut-shaped GCL region around the fovea. For each eye, the 6 sectors were coded as green (within normal limits, WNL), yellow (≤5th, ≥1st percentile), or red (<1st percentile). RESULTS: In all 94 eyes, one or more of the 6 sectors of the donut were abnormal (red or yellow), while all 6 sectors were red in 52 (55%) of the eyes. On the other hand, 33 eyes had one or more sectors WNL (green). While the pattern of donut damage varied widely across these 33 eyes, 61 of the 66 hemiretinas were consistent with a temporal-to-nasal progression of damage within each hemiretina as predicted by our model. CONCLUSIONS: All eyes with advanced glaucoma had damage to the critically important central, donut-shaped GCL region. This region showed a wide range of patterns of damage, but these patterns were consistent with the natural history of progression based on a model of macular progression. These results have implications for the clinical identification of macular progression, as well as for inclusion criteria for clinical trials seeking to preserve central macular function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".