Examining the effects of pre-pregnancy weight and gestational weight gain on allergic disease development in offspring: a protocol for a population-based study using health administrative databases in Ontario, Canada
Bibliographic record
Abstract
Introduction: Over the last 20 years, excess maternal pre-pregnancy weight (overweight and obesity) and gestational weight gain have become the most common morbidities in pregnancy. These morbidities may pose a threat to fetal immunological development through associated metabolic dysfunction and inflammation and, as such, may partly explain the concurrent rise of paediatric allergic disease. We will examine the effect of maternal pre-pregnancy weight and gestational weight gain during pregnancy on the incidence of allergic diseases among offspring in Canada's most populous province. Methods and analysis: We will conduct a retrospective, population-based cohort study of all singleton live births to residents of Ontario, Canada in 2012-2013 and 2013-2014. The study population will be defined using maternal-newborn records from the provincial birth registry, which captures information on maternal pre-pregnancy weight and gestational weight gain. The cohort will be linked with provincial health administrative databases, allowing for follow-up of neonates through early childhood until 2019 (5-7 years of age). Allergic disease development (asthma, rhinitis, atopic dermatitis and anaphylaxis) will be ascertained using diagnostic codes from healthcare encounters. Potential confounders have been identified a priori through a directed acyclic graph. Cox proportional hazards regression models will be employed to assess the associations between maternal pre-pregnancy weight, gestational weight gain and incident paediatric allergic disease. Several preplanned sensitivity analyses will be conducted, including a probabilistic bias analysis of outcome misclassification. Ethics and dissemination: Ethics approval was obtained from the Research Ethics Board of the Children's Hospital of Eastern Ontario and the ICES Privacy Office. Findings will be disseminated in scientific conference presentations and peer-reviewed publications.
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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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".