The Canadian Mother-Child Cohort Active Surveillance Initiative (CAMCCO): Comparisons between Quebec, Manitoba, Saskatchewan, and Alberta
Bibliographic record
Abstract
BACKGROUND: Given that pregnant women taking medications are excluded from clinical trials, real-world evidence is essential. We aimed to build a Canadian Mother-Child Cohort Active Surveillance Initiative (CAMCCO) and compare frequency of prematurity, low-birth-weight (LBW), major malformations, multiplicity, and gestational medication use across four provinces. METHODS: CAMCCO is a collaborative research infrastructure that uses real-world data from large provincial health care databases in Canada; developed with standardized methods to similarly construct population-based pregnancy/child cohorts with longitudinal follow-up by linking administrative/hospital/birth databases. CAMCCO also includes a common repository to i) share algorithms and case definitions based on diagnostic and procedural codes for research/training purpose, and ii) download aggregate data relevant to primary care providers, researchers, and decision makers. For this study, data from Quebec (1998-2015), Manitoba (1995-2019), Saskatchewan (1996-2020), and Alberta (2005-2018) are compared (Chi-square tests, p-values), and trends are calculated using Cochran-Armitage trend tests. RESULTS: Almost two-thirds (61%) of women took medications during pregnancy, mostly antibiotics (26%), asthma drugs (8%), and antidepressants (4%). Differences in the prevalence of prematurity (5.9-6.8%), LBW (4.0-5.2%), and multiplicity (1.0-2.5%) were statistically significant between provinces (p<0.001). Frequency of major malformations increased over time in Quebec (7-11%; p<0.001), Saskatchewan (5-11%; p<0.001), and Alberta (from 7-8%; p<0.001), and decreased in Manitoba (5-3%; p<0.001). Cardiovascular and musculoskeletal malformations were the most prevalent. INTERPRETATION: Medications are often used among Canadian pregnancies but adverse pregnancy outcomes vary across provinces. Digitized health data may help researchers and care providers understand the risk-benefit ratios related to gestational medication use, as well as province-specific trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".