Analysis of Risk and Protective Factors for Arthritis Status and Severity Using Survey Data
Bibliographic record
Abstract
This study looked at how cigarette smoking, alcohol consumption, obesity, and physical activity are associated with the prevalence and severity of arthritis among adults living in Delaware, U.S. through the analysis of survey data. We examined data from the 2009 Delaware Behavioral Risk Factor Surveillance System (BRFSS). Weighted percentages were calculated for the arthritis-related factors above by arthritis status and activity limitation due to arthritis/joint symptoms, and were analyzed using the Rao-Scott χ2 test. A multiple logistic regression analysis was performed to determine an odds ratio (OR) while adjusting for gender, age, race/ethnicity, and education. Adult Delawareans self-reporting arthritis were more likely to be former and current smokers than those without self-reported arthritis (p < 0.001; OR = 1.58 for former smokers vs. non-smokers; OR = 1.52 for current smokers vs. non-smokers). Moderate and heavy alcohol consumption was associated with lower severity of arthritis (p < 0.001; OR = 0.66 for moderate drinking vs. no drinking; OR = 0.50 for heavy drinking vs. no drinking). There was a significant relationship of obesity to both arthritis status (p < 0.001; OR = 2.13 for obesity vs. not overweight/obesity) and severity (p < 0.008; OR = 1.67 for obesity vs. not overweight/obesity). Furthermore, people having arthritis-related activity limitation were more likely to not meet the current physical activity recommendations (p = 0.013; OR = 1.46). It appears that smoking and obesity have a negative impact on the risk and severity of arthritis, whereas alcohol consumption and physical activity may be protective against arthritis. A proper analysis of survey data is essential to truly understand how human behavior impacts people’s health.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".