Influence of cannabis exposure in pregnancy on childhood health outcomes: a population-based birth cohort
Bibliographic record
Abstract
Abstract Cannabis use in pregnancy has increased, and many women continue to use it throughout pregnancy. With the legalization of recreational cannabis in many jurisdictions, there is concern about potentially adverse childhood outcomes related to prenatal exposure.4 Using the provincial birth registry containing information on cannabis use during pregnancy, we will assemble a large, population-based cohort of children born to mothers in Ontario, with and without prenatal exposure to cannabis from birth to 10 years of age. A series of investigations will examine the health effects of prenatal cannabis exposure on child outcomes using novel methods to address confounding. We will link pregnancy and birth data to provincial health administrative databases to ascertain child neurodevelopmental outcomes. The unique aspect of our proposed research is that we plan to utilize an existing population-based perinatal registry combined with administrative datasets for long-term follow up of children using a rich set of covariates and potential confounders to assess the association with cannabis exposure on pregnancy and perinatal outcomes and into childhood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".