The relationship of age with the clinical phenotype in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The multiple sclerosis (MS) clinical course and relapses frequency before progression vary widely. OBJECTIVE: To investigate the influence of age on the MS phenotype. METHODS: Among 751 primary progressive (PP = 217) and secondary progressive (SP = 534) MS patients from the London Ontario database, we assessed the relationship of age on the relapse frequency and on the progressive phase evolution, and the impact of relapses on the age at onset of progression. RESULTS: Age at onset did not influence the early attacks frequency, but patients younger at onset had larger number of total attacks before progression (age = 27.4, 31.0 and 32.8 mean years; ⩾4, 2-3 and 1 relapses, respectively) and longer latency to SP. Although frequent early relapses predicted younger age at SP onset, patients with no attacks (primary progressive multiple sclerosis (PPMS)), or 1, 2-3 and ⩾4 relapses during the relapsing-remitting phase started progressing at similar age (38.6, 41.3, 41.4 and 39.2 mean years, respectively). The age at onset of progressive phase did not affect its evolution. CONCLUSIONS: Age strongly influences the phenotype before progression. Relapsing-remitting patients younger at onset are more likely to display a predominantly inflammatory course, yet relapses number does not affect the age at onset of progression.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".