Cohort Profile: The All Our Babies pregnancy cohort (AOB)
Bibliographic record
Abstract
Why was the cohort set up? All Our Babies (AOB) is a community-based, longitudinal pregnancy cohort developed to investigate the relationships between the prenatal and early life periods and outcomes for infants, children and mothers. The design of AOB follows a life course perspective, whereby the influence of early events on long-term health and development of both mothers and children are investigated through examining factors across life stages. AOB spans pregnancy, birth and early postpartum through childhood, and therefore provides the unique opportunity to describe the relations between prenatal events and early life development and to examine key factors that influence child and mother well-being over time. AOB was originally designed to measure maternal and infant outcomes during the perinatal period, with a particular emphasis on barriers and facilitators to accessing health care services in Calgary, Alberta. Approximately 1 year after recruitment had started, an additional objective,to examine biological and environmental determinants of adverse birth outcomes, specifically spontaneous pre-term birth, was added. Recognition of the opportunity to continue to collect relevant life course information on the AOB families, collaborations with content experts and securing additional funding has enabled ongoing follow-up of AOB mother-child dyads. The overall objective was to further investigate risk and protective factors for optimal child development, and to understand the trajectory and impact of poor maternal mental health over time. Mothers have completed questionnaires from pregnancy to 3 years postpartum, and consented to providing the research team with access to their obstetric medical records. Data collection for a 5-year follow-up questionnaire is ongoing. A subgroup within the cohort participated in the ‘prediction of preterm birth’ component and provided blood samples during pregnancy and an umbilical cord blood sample. The continuation of follow-up to 8 years is under way.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".