P2-32 Vitamin D in the prediction of metabolic syndrome: a target for public health intervention
Bibliographic record
Abstract
Background The association between vitamin D (VitD) and cardiometabolic health has been described however the ability of VitD to predict metabolic syndrome (MetSyn) beyond individual demographics and common lifestyle factors is unclear. Methods 1799 adults from the Canadian Health Measures Survey, a nationally representative survey were examined. MetSyn was defined based on the National Cholesterol Education Program's Adult Treatment Panel III criteria. The comparative value of smoking status, energy expenditure from physical activity and plasma 25 (OH)D VitD to predict the presence of metabolic syndrome was evaluated using receiver operating characteristic curves (ROC) from fully adjusted logistic regression models (age, sex, ethnicity, education, annual fruit intake and month of interview). Somer's D and c statistics were used to compare across models. Sampling weights were applied to all models. Results Inclusion of plasma VitD, energy expenditure and smoking status all lead to increases in the c statistic and Somer's D suggesting increased predictive ability compared to the baseline model. ROC contrasts, however, showed that only inclusion of VitD significantly increased the predictive ability of the model (p=0.001). Discussion VitD may be a useful predictive tool for cardiometabolic risk prediction in addition to conventional factors. Prospective analyses may provide more insight into VitD as a potential population-based intervention target for reduction of chronic disease burden related to MetSyn.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".