Non-arteritic anterior ischemic and glaucomatous optic neuropathy: Implications for neuroretinal rim remodeling with disease severity
Bibliographic record
Abstract
PURPOSE: Post-acute non-arteritic ischemic optic neuropathy (NAION) and glaucomatous optic neuropathy (GON) can be difficult to differentiate clinically. Our objective was to identify optical coherence tomography (OCT) parameters to help differentiate these optic neuropathies. METHODS: We compared 12 eyes of 8 patients with NAION and 12 eyes of 12 patients with GON, matched for age and visual field mean deviation (MD). All patients underwent clinical assessment, automated perimetry (Humphrey Field Analyzer II; Carl Zeiss Meditec, Dublin, CA, USA), and OCT imaging (Spectralis OCT2; Heidelberg Engineering, Heidelberg, Germany) of the optic nerve head and macula. We derived the neuroretinal minimum rim width (MRW), peripapillary retinal nerve fibre layer (RNFL) thickness, central anterior lamina cribrosa depth, and macular retinal thickness. RESULTS: MRW was markedly thicker, both globally and in all sectors, in the NAION group compared to the GON group. There was no significant group difference in RFNL thickness, globally or in any sector, with the exception of the temporal sector that was thinner in the NAION group. The group difference in MRW increased with increasing visual field loss. Other differences observed included lamina cribrosa depth significantly greater in the GON group and significantly thinner central macular retinal layers in the NAION group. The ganglion cell layer was not significantly different between the groups. CONCLUSIONS: The neuroretinal rim is altered in a dissimilar manner in NAION and GON and MRW is a clinically useful index for differentiating these two neuropathies. The fact that the difference in MRW between the two groups increased with disease severity suggests distinct remodelling patterns in response to differing insults with NAION and GON.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".