The natural history of early versus late disability accumulation in primary progressive MS
Bibliographic record
Abstract
BACKGROUND: Primary progressive multiple sclerosis (PPMS) is the least common MS disease course and carries the worst prognosis. In relapsing-remitting multiple sclerosis (RRMS) disability accumulation occurs in two distinct phases, but it is unclear whether this is also true for PPMS. Here we investigate factors associated with early and late disability accumulation in PPMS. METHODS: We used Kaplan-Meier survival analyses and Cox regression to investigate the influence of sex, age at disease onset and onset symptoms on time to, and age at, Expanded Disability Status Scale (EDSS) 4 and 6, as well as the time from EDSS 4 to 6 in patients with PPMS. RESULTS: We identified 500 patients with PPMS. The analyses on time to EDSS 4 included 358 patients, and those on time to EDSS 6 included 392 patients. The median times to EDSS 4 and EDSS 6 were 5 and 9 years. The analyses on age at EDSS 4 included 360 patients, and those on age at EDSS 6 included 402 patients. The median ages at EDSS 4 and EDSS 6 were 51 and 55 years. Older age at onset and bilateral motor onset symptoms were independently associated with a shorter time to both EDSS 4 and EDSS 6. Sex and other onset symptoms were not associated with time to, or age at, landmark disability. Only age at onset was significantly associated with the time from EDSS 4 to EDSS 6. CONCLUSIONS: Age at disease onset is the most important predictor of disability accumulation in PPMS. Bilateral motor onset symptoms were associated with quicker disease progression. In contrast to RRMS, we found no evidence for distinct phases of disability accumulation in PPMS. Disability accumulation in PPMS appears to be affected by the same factors throughout its course.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".