Clinical and Demographic Predictors of Optic Neuritis Subtype
Bibliographic record
Abstract
To determine which clinical features differentiate acute optic neuritis (ON) subtypes to support treatment decision-making in patients when the diagnostic work-up is incomplete or inconclusive. We performed a retrospective study at two academic centers. ON was classified as idiopathic/multiple sclerosis-associated (I/MS-ON; also known as “typical” ON) versus non-I/MS-ON (e.g. neuromyelitis optica; also known as “atypical” ON). Multiple linear regression models assessed the association between ON subtype and clinical features including: demographics, presence of optic disc edema, bilaterality (simultaneous ON involving both eyes), and baseline visual acuity (logMAR). Sensitivity analyses examined the impact of incomplete race/ethnicity data on subtype. Among 614 episodes (518 patients), most ON events (n = 440, 85%) were I/MS-ON. In univariate analyses, bilaterality (OR 6.67 [95%CI 3.7,11.11]), presence of optic disc edema (OR 2.22 [95%CI 1.32,3.70]), and age (OR 1.32 [95%CI 1.08,1.61] for each decade) were significantly associated with higher odds of having non-I/MS-ON compared to I/MS-ON. In multiple logistic regression modeling, each decade of life (OR 1.35 [95%CI 1.06,1.69]), bilaterality (OR 7.69 [95%CI 4.17,14.29]), and each point increase in baseline logMAR (OR 1.47 [95%CI 1.11,1.92]) were associated with increased odds of having non-I/MS-ON compared to I/MS-ON. In sensitivity analyses, age no longer significantly predicted ON subtype. When considering multiple clinical factors, bilateral simultaneous ON and worse baseline visual acuity were significantly associated with non-I/MS-ON. Older age may also be associated with non-I/MS-ON, but additional studies are needed. These observations may guide decision-making in patients with ON, in which diagnostic testing is incomplete or inconclusive.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".