Predicting Conversion to MS - The Role of a History Suggestive of Demyelination
Bibliographic record
Abstract
BACKGROUND: The ability to predict conversion to multiple sclerosis (MS) accurately when assessing a patient with a clinically isolated syndrome (CIS) is of paramount importance. Magnetic resonance imaging (MRI) is the best paraclinical tool currently available; however the significance of a history of an event suggestive of demyelination prior to CIS presentation has not been evaluated. METHODS: A retrospective chart review of all optic neuritis cases presenting as CIS to a single neuro-ophthalmologist in London, Ontario between 1990 to 1998 was performed. Data were collected regarding demographics, past medical history, history of present illness, and family history. Conversion to MS was determined by the McDonald criteria after ten years of follow-up. Bayesian statistics and logistic regression were used to determine the best predictors of conversion to MS from CIS. RESULTS: One hundred and sixteen optic neuritis subjects were included in the analysis. After ten years, 42.2% had converted to MS. The best predictor of future conversion remained at least one brain lesion, disseminated in space, on MRI (sensitivity 0.90, specificity 0.75). However, if the subject additionally had a history suggestive of a demyelinating event in the past that had not been confirmed clinically, the specificity increased to 0.96. These two traits taken together had an odds ratio of 27.8 for conversion to MS in the next ten years (p<0.001). CONCLUSIONS: A history of an event suggestive of demyelination prior to presenting with optic neuritis as CIS increases the ability of the clinician to predict conversion to MS in the next ten years.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| 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".