Design, methods and demographics from phase I of Alberta's Tomorrow Project cohort: a prospective cohort profile
Bibliographic record
Abstract
BACKGROUND: Prospective cohorts have the potential to support multifactorial, health-related research, particularly if they are drawn from the general population, incorporate active and passive follow-up and permission is obtained to allow access by researchers to data repositories. This paper describes Phase I of the Alberta's Tomorrow Project cohort, a broad-based research platform designed to support investigations into factors that influence cancer and chronic disease risk. METHODS: Adults aged 35-69 years living in Alberta, Canada, with no previous cancer diagnosis other than nonmelanoma skin cancer were recruited to the project by telephone-based random digit dialling. Participants were enrolled if they returned a Health and Lifestyle Questionnaire. Past year diet and physical activity questionnaires were mailed 3 months after enrolment. Consent was sought for active follow-up and linkage with administrative databases. Depending on enrolment date, participants were invited to complete up to 2 follow-up questionnaires (2004 and 2008). RESULTS: Between 2001 and 2009, 31 072 (39% men) participants (mean age 50.2 [± 9.2] yr) were enrolled and 99% consented to linkage with administrative databases. Participants reported a wide range of educational attainment and household income. Compared with provincial surveillance data from the Canadian Community Health Survey, Alberta's Tomorrow Project participants had higher body mass index, lower prevalence of smoking and similar distribution of chronic health conditions. Follow-up questionnaires were completed by 83% and 72% of participants in 2004 and 2008, respectively. Robust quality control measures resulted in low frequencies of missing data. INTERPRETATION: Alberta's Tomorrow Project provides a robust platform, based on a prospective cohort design, to support research into risk factors for cancer and chronic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".