Widening inequality in extreme macrosomia between Indigenous and non‐Indigenous populations of Québec, Canada
Bibliographic record
Abstract
OBJECTIVE: To evaluate trends in macrosomia by severity in Indigenous vs. non-Indigenous populations of Québec, Canada. METHODS: We used a retrospective cohort of 2,298,332 singleton live births in the province of Québec, 1981-2008. Indigenous births were identified by community of residence (First Nations, Inuit, non-Indigenous) and language spoken (First Nations, Inuit, French/English). High birth weight (HBW) and large-for-gestational-age (LGA) births were categorised by severity (moderate, very, extreme). Time trends in HBW and LGA, by severity, were estimated using odds ratios (OR) and rate differences for Indigenous vs. non-Indigenous births, adjusting for maternal characteristics. RESULTS: Relative to non-Indigenous, First Nations (but not Inuit) had higher rates of extreme HBW (1.3% vs. 0.1%) and extreme LGA birth (12.6% vs. 2.2%), and rates increased over time. First Nations had progressively elevated ORs with greater severity of macrosomia, and associations were strongest for extreme HBW >5,000 g (OR=12.4) and LGA >97th percentile (OR=7.2). CONCLUSION: Inequalities in extreme macrosomia between First Nations and non-Indigenous Quebecers are pronounced and widened between 1981 and 2008. IMPLICATIONS: Studies are needed to determine why macrosomia rates are increasing in Québec's First Nations, and how they compare with Indigenous sub-groups of demographically similar countries, including Australia and New Zealand.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".