Risk of relapse phenotype recurrence in multiple sclerosis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".