Population-based birth cohort study on diabetes in pregnancy and infant hospitalisations in Cree, other First Nations and non-Indigenous communities in Quebec
Bibliographic record
Abstract
OBJECTIVES: Diabetes in pregnancy, whether pre-gestational (chronic) or gestational (de novo hyperglycaemia), increases the risk of adverse birth outcomes. It is unclear whether gestational diabetes increases the risk of postnatal morbidity in infants. Cree First Nations in Quebec are at high risk for diabetes in pregnancy. We assessed whether pre-gestational or gestational diabetes may increase infant hospitalisation (an infant morbidity indicator) incidence, and whether this may be related to more frequent infant hospitalisations in Cree and other First Nations in Quebec. DESIGN: Population-based birth cohort study through administrative health data linkage. SETTING AND PARTICIPANTS: Singleton infants (≤1 year) born to mothers in Cree (n=5070), other First Nations (9910) and non-Indigenous (48 200) communities in rural Quebec. RESULTS: Both diabetes in pregnancy and infant hospitalisation rates were much higher comparing Cree (23.7% and 29.0%) and other First Nations (12.4% and 34.1%) to non-Indigenous (5.9% and 15.5%) communities. Compared with non-diabetes, pre-gestational diabetes was associated with an increased risk of any infant hospitalisation to a greater extent in Cree and other First Nations (relative risk (RR) 1.56 (95% CI 1.28 to 1.91)) than non-Indigenous (RR 1.26 (1.15 to 1.39)) communities. Pre-gestational diabetes was associated with increased risks of infant hospitalisation due to diseases of multiple systems in all communities. There were no significant associations between gestational diabetes and risks of infant hospitalisation in all communities. The population attributable risk fraction of infant hospitalisations (overall) for pre-gestational diabetes was 6.2% in Cree, 1.6% in other First Nations and 0.3% in non-Indigenous communities. CONCLUSIONS: The study is the first to demonstrate that pre-gestational diabetes increases the risk of infant hospitalisation overall and due to diseases of multiple systems, but gestational diabetes does not. High prevalence of pre-gestational diabetes may partly account for the excess infant hospitalisations in Cree and other First Nations communities in Quebec.
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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 | 0.003 |
| 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) | 0.002 | 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".