Why are babies in Canada getting smaller?
Bibliographic record
Abstract
INTRODUCTION: Recent evidence from the United States and Canada suggests an unexplained increase in small-for-gestational-age (SGA) births (<10th percentile). This study aimed to identify reasons for the recent increase in SGA births in Canada. DATA AND METHODS: Using Canada's Vital Statistics - Birth Database, the study population included all singleton live births, 2000 to 2016, inclusive. Temporal changes in birth weight (grams), birth weight for gestational age z-scores, and SGA births were examined. Multivariable logistic regression was used to determine if the ncreased risk of an SGA birth over time was eliminated or attenuated by adjusting for selected individual and sociodemographic factors that have previously been associated with SGA births. RESULTS: There were 5,941,820 singleton live births in Canada between 2000 and 2016. Mean birth weight for all births decreased from 3,442 grams in 2000, to 3,367 grams in 2016, while SGA birth increased from 7.2% in 2000 to 8.0% in 2016. The multivariable model showed higher odds of SGA birth among births to parents born outside of Canada, unmarried women, older women, nulliparous women and women residing in low income neighborhoods. After adjusting for sociodemographic factors, the crude 12% increase in odds of SGA birth in 2016 compared to 2000 (95% Confidence Interval (CI): [10 to 14%]) was attenuated, ut not eliminated (adjusted odds ratio for calendar time 1.08 (95% CI: [1.06, 1.10])). INTERPRETATION: This study identified a decrease in fetal size in Canada between 2000 and 2016. The rise in SGA births in Canada was explained only partly as a result of concurrent changes in the demography of childbirth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".