Risk Factors for Preterm Birth and Small‐for‐gestational‐age Births among <scp>C</scp>anadian Women
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it