Skewness of Fractional Anisotropy Detects Decreased White Matter Integrity Resulting From Acute Optic Neuritis
Bibliographic record
Abstract
PURPOSE: Diffusion tensor imaging (DTI) has demonstrated optic nerve damage associated with optic neuritis (ON); however, the usefulness of mean fractional anisotropy (FA) specifically is varied in the literature. We wished to determine whether histogram analysis of FA better detects ON damage than mean FA. METHODS: The ON patients (n = 24) underwent DTI within 1 month of symptoms and then 6 months later (n = 21). Twelve control subjects participated in one session. Mean FA and axial (AD), radial (RD), and mean (MD) diffusivities were compared between ON and fellow eyes, control eyes, and sessions. Values were sorted into bins, and coefficients of skewness of FA, AD, RD, and MD were compared between ON and fellow eyes, control eyes, and sessions. RESULTS: Mean AD, RD, and MD of ON eyes were significantly reduced compared with fellow eyes (P < 0.04) within 1 month of symptoms, but did not differ at 6 months. Mean AD and RD of ON eyes were significantly lower than control eyes (P < 0.05). No differences were observed for mean FA. The coefficient of skewness of FA was significantly different between ON eyes and fellow eyes (P = 0.03) and control eyes (P = 0.04) within 1 month of symptoms, but did not differ at 6 months. No differences were observed for AD, RD, and MD. CONCLUSIONS: Skewness of FA can detect white matter damage associated with ON and its recovery, which may further inform us of how DTI can measure white matter injury and repair.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".