High frequency of adverse health behaviors in multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it