The Eastern Caribbean Health Outcomes Research Network (ECHORN) Cohort Study: Design, Methods, and Baseline Characteristics
Bibliographic record
Abstract
Noncommunicable diseases (NCDs) account for a higher proportion of mortality and morbidity in the Caribbean and US territories-majority-minority communities-than in the United States or Canada. Strategies to address this disparity include enhancing data collection efforts among racial/ethnic communities. The ECHORN Cohort Study (ECS), a regional adult cohort study, estimates prevalence and assesses risk factors for NCDs in two United States territories and two Caribbean islands. Here, we describe the cohort study approach, sampling methods, data components, and demographic makeup for wave one participants. We enrolled ECS participants from each participating island using random and probability sampling frames. Data components include a clinical examination, laboratory tests, a brief clinical questionnaire, and a self-administered health survey. A subset of ECS participants provided a blood sample to biobank for future studies. Approximately 2961 participants were enrolled in wave one of the ECS. On average, participants are 57 years of age, and the majority self-identify as female. Data from the ECS allow for comparisons of NCD outcomes among racial/ethnic populations in the US territories and the US and evaluations of the impact of COVID-19 on NCD management and will help highlight opportunities for new research.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".