The Value of Population Cohorts and Biobank Resources to Address Environmental Health Issues: The Cartagene Platform
Bibliographic record
Abstract
Chronic diseases result from a combination of individual genetic predisposition and exposure to environmental risk factors. The joint assessment of exposure and disease outcome is the key to accelerate breakthroughs in environmental health research. The selection of reliable and unbiased indicators are necessary to carry out high quality research but remains a big challenge. Large population cohorts and biobanks can provide high quality data for the assessment of both exposure and health effects. The CARTaGENE (CaG) cohort is the largest prospective health study in Québec. Since 2007, CaG recruited 43,000 participants, aged 40-69 years at baseline. CaG is characterized by a wealth of collected data on each participant: health questionnaire (lifestyle, mental health, etc.), physical measures (blood pressure, spirometry, electrocardiogram, etc.), food frequency questionnaire, residential and occupational histories, biochemical measures (eg. lipids, glycated hemoglobin, creatinine), and genotyping data. The CaG biobank contains biosamples (blood, plasma, urine, etc,) for more than 30,000 participants. These biosamples allow for the measurements of additional biomarkers (exposure or effect) for specific purposes. CaG databases are linked to administrative health databases which are of great value to inform about health care use and, along with prospective follow-up questionnaires, enable to collect data on a continuous manner to identify temporal variations. CaG is fully integrated in the Canadian Partnership for Tomorrow Project, which represents 300,000 participants and more than 150,000 biological samples available for health research. CaG was created to support the scientific community in identifying the determinants of chronic diseases of environmental and/or genetic origin. Longitudinal population cohort such as CaG offers very powerful tools for environmental epidemiology studies.
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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 | 0.129 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.015 |
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".