Preterm delivery among Inuit women in the Baffin Region of the Canadian Arctic
Bibliographic record
Abstract
OBJECTIVE: To evaluate the rate and causes of preterm (before 37 weeks gestation) and very preterm (before 32 weeks gestation) delivery among a population of Inuit living in Canada. STUDY DESIGN: Three-year retrospective cross-sectional review of charts for patients delivering in the Baffin Region of Canada. RESULTS: There were 938 births over the study period; 95% to Inuit women. Inuit women had a preterm delivery rate of 18.2% and a very preterm delivery rate of 2.4%, more than twice the Canadian national average. Sociodemographic risk factors for preterm delivery including substance use, young age, single marital status, and poor nutrition, occurred more frequently among Inuit women compared to non-Inuit women, but were not independently associated with prematurity. Known medical and obstetrical risk factors were associated with preterm delivery among Inuit women; history of prior preterm delivery, multiple pregnancy, placenta previa, poor weight gain and vaginal bleeding after 20 weeks gestation. Hospitalization rates and infant mortality were higher among preterm infants. The most common indication for hospitalization was respiratory infection (51.1%) followed by other infection (15.8%). CONCLUSION: Inuit women had preterm and very preterm delivery rates more than twice the Canadian national average. Preterm delivery was associated with several medical risk factors and resulted in significant increases in infant hospitalization and mortality.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".