High frequency of adverse health behaviors in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Health behaviors influence chronic disease risks in the general population, and may influence health outcomes independently of comorbid diseases. Health behaviors receive less attention in multiple sclerosis (MS) than in the general population. We assessed health behaviors among participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry and the demographic characteristics associated with particular health behaviors. METHODS: In October 2006, we surveyed NARCOMS participants regarding smoking using questions from the Behavioral Risk Factor Surveillance Survey; physical activity using questions from the PEPI study, alcohol use using the AUDIT-C; and height and weight. To determine the independent demographic predictors of health behaviors, we used multivariable logistic regression, either binary or polytomous as appropriate. RESULTS: Of 8983 responders, 4867 (54.2%) ever smoked; 1542 (17.3%) currently smoked. On the basis of the AUDIT-C, 1632 (18.2%) were at risk for alcohol abuse or dependence. A quarter of participants were obese (n = 2269), and 2780 (31.3%) were overweight. Fewer than 25% of participants reported moderate or heavy leisure-time physical activity. Generally, lower socioeconomic status was associated with a higher frequency of adverse health behaviors accounting for other demographic factors. With increasing levels of disability, the reported intensity of physical activity was lower, and the frequency of overweight or obesity was higher. CONCLUSIONS: Patients with MS exhibit frequent adverse health behaviors, increasing the risk of other chronic diseases. Further research is needed to determine how these behaviors influence disability progression, quality of life, and other MS-related outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| 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".