Assessment of Outcome Predictors after First Attack of Optic Neuritis
Bibliographic record
Abstract
BACKGROUND: Optic Neuritis (ON) is one of the most common clinically isolated syndromes which develops into clinically diagnosed Multiple Sclerosis (CDMS) over time. OBJECTIVE: To assess the conversion rate of Iranian patients presenting with idiopathic ON to CDMS as well as monitoring potential demographic and clinical risk factors. METHODS: Atotal of 219 patients' medical records of idiopathic ON from March 2001 to May 2009 were reviewed. Demographic findings, ophthalmologic characteristics on admission and discharge, diagnostic approaches, type and dosage of therapy were retrospectively reviewed. A structured telephone interview was then conducted to identify patients who had subsequently been diagnosed with MS. Survival analysis was used to evaluate the cumulative probability of MS conversion and contributory risk factors. RESULTS: From the 219 ON patients, 109 [age 11-51, female: 81%] were followed up. Among the male gender the mean age of patients developing MS was significantly lower (P=0.01). In cox regression model, female sex (p=0.07), bilateral ON (p=0.003), MRI abnormalities (p <0.001) and high dose (5g) corticosteroid therapy (p<0.001) were identified as risk factors for the development of MS. The two and five year cumulative probability of developing MS were 27% and 45%, respectively. CONCLUSIONS: Idiopathic ON in Iranian patients carries higher risk of progression to MS compared to other Asian countries. MRI lesions are the strongest independent risk factor of developing CDMS. Bilateral ON, female gender and high dose corticosteroid therapy are also important factors in predicting CDMS development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".