Population-based cohort development in Alberta, Canada: a feasibility study.
Bibliographic record
Abstract
In a climate of increasing privacy concerns, the feasibility of establishing new cohorts to examine chronic disease etiology has been debated. Our primary aim was to ascertain the feasibility of enrolling a geographically dispersed, population-based cohort in Alberta. We also examined whether enrolees would grant access to provincial health care utilization data and consider providing blood for future analysis. Using random digit dialling, 22,652 men and women aged 35 to 69 years, without diagnosed cancer, were recruited. Of these, 52.4 percent (N=11,865) enrolled; 84 percent of Alberta communities were represented. Approximately 97 percent of enrolees consented to linkage with health care data, and 91 percent indicated willingness to consider future blood sampling. Comparisons between the cohort and the Canadian Community Health Survey (Cycle 1.1) for Alberta demonstrated similarities in marital status and income. However, the cohort had a smaller proportion who had not finished high school, a greater proportion of nonsmokers and a higher prevalence of obesity. These findings indicate that establishment of a geographically dispersed cohort is feasible in the Canadian context, and that data linkage and biomarker studies may be viable.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".