The Ontario Birth Study: A prospective pregnancy cohort study integrating perinatal research into clinical care
Bibliographic record
Abstract
BACKGROUND: Pregnancy and early childhood represent critical periods that impact health throughout the life-course. The Ontario Birth Study (OBS) is a pregnancy cohort study designed as a platform for research on pregnancy complications, maternal and infant health, and the developmental origins of health and disease. METHODS: Pregnant women <17 weeks gestational age were recruited between 2013 and 2015 from antenatal clinics at Mount Sinai Hospital, Toronto, Canada. Life style and diet questionnaires, biospecimens, and clinical data were collected throughout the pregnancy and postpartum period at the time of clinical care. The OBS was integrated into clinical care to reduce participant burden, improve efficiency, and increase research potential. RESULTS: There were 3181 eligible women approached for recruitment and 1374 (43%) participated in the study. Among the 1374 participants, 1272 (93%) delivered a liveborn infant and were followed to 6-10 weeks postpartum. Of the 1272 women who completed the study, 98% had at least one pregnancy blood sample collected, 97% had vaginal swabs collected, 90% completed the prenatal life style questionnaires, and 78% completed the Diet History Questionnaire. Most women (88%) were ≥30 years of age, 55% had no previous children, 24% were overweight or obese pre-pregnancy and 78% of parents had postsecondary education. Most pregnancies were singleton (3% twins), 34% delivered by caesarean section, and 6% preterm (<37 weeks gestation). CONCLUSIONS: The OBS is a contemporary cohort with detailed data including banked biospecimens for studies of pregnancy health and the gene-environment interactions that establish developmental trajectories to health, learning, and social functioning.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".