Aboriginal birth cohort (ABC): a prospective cohort study of early life determinants of adiposity and associated risk factors among Aboriginal people in Canada
Bibliographic record
Abstract
BACKGROUND: Aboriginal people living in Canada have a high prevalence of obesity, type 2 diabetes, and cardiovascular disease (CVD). To better understand the pre and postnatal influences on the development of adiposity and related cardio-metabolic factors in adult Aboriginal people, we will recruit and follow prospectively Aboriginal pregnant mothers and their children - the Aboriginal Birth Cohort (ABC) study. METHODS/DESIGN: We aim to recruit 300 Aboriginal pregnant mothers and their newborns from the Six Nations Reserve, and follow them prospectively to age 3 years. Key details of environment and health including maternal nutrition, glucose tolerance, physical activity, and weight gain will be collected. At birth, cord blood and placenta samples will be collected, as well as newborn anthropometric measurements. Mothers and offspring will be followed annually with serial measurements of diet and physical activity, growth trajectory, and adiposity. DISCUSSION: There is an urgent need to understand maternal and child factors that underlie the early development of adiposity and type 2 diabetes in Aboriginal people. The information generated from this cohort will assist the Six Nations community in developing interventions to prevent early adiposity in Aboriginal children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".