Predicting serum vitamin D concentrations based on self-reported lifestyle factors and personal attributes
Bibliographic record
Abstract
Evidence supports the role of vitamin D in various conditions of development and ageing. Serum 25-hydroxyvitamin D (25(OH)D) is the best indicator for current vitamin D status. However, the cost of its measurement can be prohibitive in epidemiological research. We developed and validated multivariable regression models that quantified the relationships between vitamin D determinants, measured through an in-person interview, and serum 25(OH)D concentrations. A total of 200 controls participating in a population-based case-control study in Montreal, Canada, provided a blood specimen and completed an in-person interview on socio-demographic, reproductive, medical and lifestyle characteristics and personal attributes. Serum 25(OH)D concentrations were quantified by liquid chromatography-tandem MS. Multivariable least squares regression was used to build models that predict 25(OH)D concentrations from interview responses. We assessed high-order effects, performed sensitivity analysis using the lasso method and conducted cross-validation of the prediction models. Prediction models were built for users and non-users of vitamin D supplements separately. Among users, alcohol intake, outdoor time, sun protection, dose of supplement use, menopausal status and recent vacation were predictive of 25(OH)D concentrations. Among non-users, BMI, sun sensitivity, season and recent vacation were predictive of 25(OH)D concentrations. In cross-validation, 46-47 % of the variation in 25(OH)D concentrations were explained by these predictors. In the absence of 25(OH)D measures, our study supports that predicted 25(OH)D scores may be used to assign exposure in epidemiological studies that examine vitamin D exposure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".