Risk of relapse phenotype recurrence in multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: The aim was to analyse risk of relapse phenotype recurrence in multiple sclerosis and to characterise the effect of demographic and clinical features on this phenotype. METHODS: Information about relapses was collected using MSBase, an international observational registry. Associations between relapse phenotypes and history of similar relapses or patient characteristics were tested with multivariable logistic regression models. Tendency of relapse phenotypes to recur sequentially was assessed with principal component analysis. RESULTS: Among 14,969 eligible patients (89,949 patient-years), 49,279 phenotypically characterised relapses were recorded. Visual and brainstem relapses occurred more frequently in early disease and in younger patients. Sensory relapses were more frequent in early or non-progressive disease. Pyramidal, sphincter and cerebellar relapses were more common in older patients and in progressive disease. Women presented more often with sensory or visual symptoms. Men were more prone to pyramidal, brainstem and cerebellar relapses. Importantly, relapse phenotype was predicted by the phenotypes of previous relapses. (OR = 1.8-5, p = 10(-14)). Sensory, visual and brainstem relapses showed better recovery than other relapse phenotypes. Relapse severity increased and the ability to recover decreased with age or more advanced disease. CONCLUSION: Relapse phenotype was associated with demographic and clinical characteristics, with phenotypic recurrence significantly more common than expected by chance.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it