Impact of Obesity on Perinatal Outcomes among Asthmatic Women
Bibliographic record
Abstract
BACKGROUND: Only one study has investigated the combined effect of maternal asthma and obesity on perinatal outcomes; however, it did not consider small-for-gestational age and large-for-gestational age infants. OBJECTIVES: To examine the impact of obesity on perinatal outcomes among asthmatic women. METHODS: A cohort of 1386 pregnancies from asthmatic women was reconstructed using three of Quebec's administrative databases and a questionnaire. Women were categorized using their prepregnancy body mass index. Underweight, overweight and obese women were compared with normal weight women. The primary outcome was the birth of a small-for-gestational-age infant, defined as a birth weight below the 10th percentile for gestational age and sex. Secondary outcomes were large-for-gestational-age infants (birth weight >90th percentile for gestational age) and preterm birth (<37 weeks' gestation). Logistic regression models were used to obtain the ORs of having small-for-gestational-age infants, large-for-gestational-age infants and preterm birth as a function of body mass index. RESULTS: The proportions of underweight, normal weight, overweight and obese women were 10.8%, 53.3%, 19.7% and 16.2%, respectively. Obese asthmatic women were not found to be significantly more at risk for giving birth to small-for-gestational-age infants (OR 0.6 [95% CI 0.4 to 1.1]), large-for-gestational-age infants (OR 1.2 [95% CI 0.7 to 2.2]) or having a preterm delivery (OR 0.7 [95% CI 0.4 to 1.3]) than normal-weight asthmatic women. CONCLUSIONS: No significant negative interaction between maternal asthma and obesity on adverse perinatal outcomes was observed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".