Diffusion-weighted imaging and retinal oximetry as potential biomarkers of visual outcomes after optic neuritis
Bibliographic record
Abstract
To elucidate the mechanisms influencing visual function recovery after optic neuritis (ON), this study employed a multicompartment diffusion weighted imaging (DWI) model to assess the role of optic radiation integrity and its relationship with retinal parameters, including automatic retinal oximetry and retinal nerve fiber layer (RNFL) thinning. Twenty-four patients with the first episode of acute unilateral ON were compared with 56 healthy volunteers with normal vision. Additionally, longitudinal analysis 3 and 6 months after ON was performed in 17 patients. Multivariate analysis of variance across baseline DWI metrics revealed a greater secondary partial volume fraction (f2) in patients. In the longitudinal analysis, a multivariate effect of time was observed only when adjusted for the affected side and time since onset; however, univariate post hoc tests were nonsignificant. An unadjusted model stratified according to clinical outcomes (best-corrected visual acuity [BCVA] and contrast sensitivity) indicated lower overall fractional anisotropy (FA) in patients with incomplete recovery. In the correlation analysis, baseline FA and oximetry (venous saturation and arteriovenous difference) predicted follow-up BCVA, whereas axial diffusivity predicted follow-up oximetry. In turn, baseline oximetry predicted follow-up RNFL thickness. In summary, DWI and retinal oximetry are both potential predictors of visual function outcomes after ON.
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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 | 0.004 |
| 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) | 0.000 | 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".