The effect of smoking on the symptoms and progression of multiple sclerosis: a review
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder of the central nervous system with characteristic demyelinating lesions and axonal loss. MS accounts for the most common cause of neurological disability in young adults in the Western world. The clinical manifestations and the course of MS are highly variable. The early stage of the disease is usually characterized by attacks of neurological dysfunction with complete or incomplete recovery, however, with time disability accumulates in many patients. MS is believed to result from an interplay between susceptibility genes and environmental factors, one of which is smoking. Smoking, a worldwide epidemic, can be regarded as an important risk factor for MS particularly because of its modifiable nature in the quest to prevent or temper the disease course in MS as well as providing possible insights into MS pathogenesis. There are also reports that smoking may influence the symptoms and disease progression in patients with MS. The purpose of this article is to review the effects of smoking on MS symptoms and progression. We conclude that (1) although there are some early reports on worsening of MS symptoms by smoking, the existing evidence is insufficient to thoroughly assess the effects of smoking on the myriad of MS symptoms and (2) smoking seems to adversely influence disease progression in MS patients. We also discuss the potential biological mechanisms linking smoking and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".