Local Glaucomatous Defects of the Circumpapillary Retinal Nerve Fiber Layer Show a Variety of Patterns of Progression
Bibliographic record
Abstract
PRECIS: The region of glaucomatous progression, seen on optical coherence tomography (OCT) images of the circumpapillary retinal nerve fiber layer (cRNFL), increases in width and depth in all eyes, but shows a variety of different patterns of loss across eyes. PURPOSE: The purpose of this study was to examine the patterns of cRNFL loss secondary to glaucomatous progression in a region associated with the superior hemifield of the 24-2/30-2 visual field (VF). METHODS: Twenty-four eyes (20 patients) with a diagnosis of glaucoma and evidence of progression on OCT had OCT disc cube scans on at least 3 separate visits (mean follow-up 7.4 y; range: 3.9 to 11.4). Circumpapillary b-scans were derived after enface images were aligned to assure that the study region (ie, 0 to -135 degrees, where 0 degree is 9 o'clock, on a right eye) coincided. Within this region, a region of progression (ROP) was defined based on the loss in cRNFL thickness between the first and subsequent visits. The width of the ROP was determined, along with the locations of its leading (close to fixation) and trailing edges. In addition, for each ROP, the location and depth at the point of maximal loss, total loss, and average remaining retinal nerve fiber layer were measured. RESULTS: The ROP proceeded both toward and away from fixation. Across eyes, the ROP varied widely in width (32 to 131 degrees, mean 82.7 degrees), location, and loss at point of deepest loss (22 to 99 μm, mean 52.9 μm), as well as total cRNFL loss. CONCLUSIONS: All eyes showed a widening and deepening of the ROP, but a variety of different patterns of progressive cRNFL loss. Thus, one should expect considerable variation in patterns of VF loss. Furthermore, conventional metrics (global or quadrant cRNFL thickness) do not fully depict the progressive changes that can be appreciated by inspecting OCT images.
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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 | 0.001 | 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".