Cohort profile of the CARTaGENE study: Quebec’s population-based biobank for public health and personalized genomics
Bibliographic record
Abstract
The CARTaGENE (CaG) study is both a population-based biobank and the largest ongoing prospective health study of men and women in Quebec. In population-based cohorts, participants are not recruited for a particular disease but represent a random selection among the population, minimizing the need to correct for bias in measured phenotypes. CaG targeted the segment of the population that is most at risk of developing chronic disorders, that is 40-69 years of age, from four metropolitan areas in Quebec. Over 20,000 participants consented to visiting 1 of 12 assessment sites where detailed health and socio-demographic information, physiological measures and biological samples (blood, serum and urine) were captured for a total of 650 variables. Significant correlations of diseases and chronic conditions are observed across these regions, implicating complex interactions, some of which we describe for major chronic conditions. The CaG study is one of the few population-based cohorts in the world where blood is stored not only for DNA and protein based science but also for gene expression analyses, opening the door for multiple systems genomics approaches that identify genetic and environmental factors associated with disease-related quantitative traits. Interested researchers are encouraged to submit project proposals on the study website (www.cartagene.qc.ca).
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".