Smokers with MS have greater decrements in quality of life and disability than non-smokers
Bibliographic record
Abstract
Background: Tobacco smoke plays a pathogenic role in multiple sclerosis (MS) and may accelerate disease progression, yet, some people with MS continue to smoke after disease onset. The average smoker reports diminished health-related quality of life (HRQOL) across many populations. Objectives: To describe the relationships between smoking status and HRQOL, disease activity, and global disability in a US population with MS. Methods: We compared smokers to non-smokers in 950 responders to the Spring 2014 update survey completed by North American Research Committee on Multiple Sclerosis (NARCOMS) registry participants. HRQOL was assessed using Short Form-12 version 2 (SF-12v2), disease activity was investigated using eight Performance Scales (PS) and three Functionality Scales (FS). Global disability was evaluated using Patient Determined Disease Steps (PDDS) and an item response theory (IRT) summed score based on the PS and FS. Results: Smokers had lower HRQOL ( p < 0.0001), reported more disease activity ( p < 0.05) and greater deficits in all PS and FS ( p = 6 × 10 −7 to 0.05), except mobility. Smokers and non-smokers did not differ by PDDS but had substantially greater IRT global disability ( p = 2 × 10 −7 ). Conclusion: Active smoking is meaningfully associated with deficits across multiple domains in people with MS and adds to the growing literature of the need for MS-tailored smoking cessation programs.
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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 | 0.002 |
| 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) | 0.006 | 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".