Smoking does not influence disability accumulation in primary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The modifiable risk factor cigarette smoking has been associated with an increased risk of developing multiple sclerosis (MS) and with disease activity in relapsing-remitting MS. However, less is known about the effect of smoking on disease progression in progressive MS. Here the association between cigarette smoking and disability accumulation in primary progressive MS (PPMS) is investigated. METHODS: Kaplan-Meier survival analyses and Cox proportional hazard modelling were used to investigate the influence of cigarette smoking on the risk of reaching Expanded Disability Status Scale (EDSS) 4 and 6 as well as the time from EDSS 4 to 6 in patients with PPMS. RESULTS: In all, 416 patients with PPMS and available smoking history were identified. Median time to EDSS 4 was 4 years in ever-smokers and 5 years in never-smokers (P = 0.27), and it was 9 years to EDSS 6 in both ever-smokers and never-smokers (P = 0.48). Smokers were not at increased risk of faster progression to EDSS 4, 6 and from EDSS 4 to 6. Age at disease onset was the strongest risk factor for progression to EDSS 4, 6 and from EDSS 4 to 6. CONCLUSIONS: Our investigation of a large and well-characterized population based PPMS cohort suggests that cigarette smoking does not influence disability accumulation in PPMS. Our findings support the idea that PPMS is driven by different underlying pathomechanisms than relapsing-remitting MS.
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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 | 0.000 | 0.000 |
| 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".