Risk Factors for Preterm Birth and Small‐for‐gestational‐age Births among <scp>C</scp>anadian Women
Bibliographic record
Abstract
BACKGROUND: Preterm births (PTB) and small-for-gestational-age (SGA) births are distinct but related pregnancy outcomes, with differing aetiologies and short and long-term morbidities. Few studies have compared a broad array of predictors among these two outcomes. The purpose of this study was to compare risk factors for PTB and SGA births using a national sample of Canadian women. METHODS: We analysed data from the Canadian Maternity Experiences Survey (n = 6421). Mothers were ≥ 15 years of age, gave birth to a singleton infant and were living with their infant at the time of the interview (between 5 and 14 months post-partum). Backward stepwise multivariable logistic regression models were constructed for each outcome. RESULTS: Risk profiles for the two outcomes had both differences and similarities. Risk factors specific to PTB were education less than high school, having a previous medical condition, developing a new medical condition or health problem during pregnancy, being a primigravida, or being a multigravida with a previous PTB or a previous miscarriage or abortion. Risk factors unique to SGA were low pre-pregnancy body mass index (<18 kg/m(2) ), smoking during pregnancy and being a recent immigrant. Risk factors for both outcomes included low weight gain during pregnancy (<9.1 kg), short stature (<155 cm) and reporting life as 'very stressful' in the year prior to birth of the baby. CONCLUSION: A greater understanding of the risk factors related to PTB and SGA may help to reduce the prevalence of these conditions and the associated risk of infant mortality and morbidity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".