Ganglion Cell Damage and Functional Recovery after Optic Neuritis (S48.002)
Bibliographic record
Abstract
Objective:To describe optic neuritis (ON) recovery based on neuronal damage in the retina. Background:ON is characterized by inflammation of the optic nerve and retrograde axonal and neuronal damage in the retina. In optical coherence tomography (OCT) the retinal nerve layer swells during the acute phase. In the post-acute phase RNFL and ganglion cell layer thin as a surrogate for axonal loss and neurodegeneration. During the acute phase, ON regularly leads to severe vision loss in the affected eye. Recovery from optic neuritis is defined as reconstitution of visual function after an acute ON. Methods:Prospective study in patients with acute optic neuritis. 56 patients (46 female and 10 male, age 36±9 years) with acute ON were followed over median 371 (25 - 975) days. Spectral domain OCT was performed on each visit, usually a few weeks apart. Best-corrected visual acuity (BCVA) was measured monocularly by the means of Snellen charts. Neuronal damage was determined as ganglion cell and inner plexiform layer (GCIPL) loss. Results:Twenty-six patients showed no GCIPL loss after acute ON, defined as in the range of 2x the standard deviation of unaffected eyes. Eyes with GCIPL loss were divided by the median (-15 µm) in eyes with moderate (n=16) and eyes with severe GCIPL loss (n=14). Male sex was associated with a higher GCIPL loss, whereas there was no influence of age. During the acute phase, eyes with later severe GICPL loss had worse BCVA, but the BCVA between moderate and no-loss eyes was similar. Eyes with more severe ON tended towards higher residual reduction of visual function. Conclusions:Recovery of ON can be defined by objective measurement of GCIPL thickness in the retina. It will be important to define factors predicting ON severity based on this classification.
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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 | 0.000 |
| 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 | 0.001 |
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".