Utilizing harmonization and common surveillance methods to consolidate 4 cohorts: the Western Alaska Tribal Collaborative for Health (WATCH) study
Bibliographic record
Abstract
BACKGROUND: According to health status reports, chronic disease prevalence appears to be rising in western Alaska Native (AN) people, and accurate population-based data are needed. Four cohort studies of western AN people were conducted in the Norton Sound and Yukon-Kuskokwim regions, but none have been large enough to allow reliable estimates of rates of chronic diseases and evaluate their risk factors. OBJECTIVE: In this article, the methods used to combine 4 major cohort studies of rural western AN people are described and the benefits and challenges encountered in combining data and standardizing surveillance methods for these studies are discussed. DESIGN: Tribal permission was obtained for each cohort study and the consolidated study. Data from baseline exams were directly combined or harmonized into new variables. Common surveillance methods were developed and implemented to identify incidence and risk factors for cardiovascular disease (CVD) events and type 2 diabetes. RESULTS: A cohort of 4,569 western AN participants (2,116 men and 2,453 women), aged 18-95 years, was established to study CVD and diabetes prevalence. Prospective surveillance data over an average 6.7-year follow-up can now be used to study CVD and diabetes incidence and associated risk factors in a subset of 2,754 western AN participants (1,218 men and 1,536 women) who consented to initial surveillance. CONCLUSIONS: The combined cohort provides statistical power to examine incidence rates and risk factors for CVD and diabetes and allows for analyses by geographic region. The data can be used to develop intervention programmes in these populations and others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".