How weight during pregnancy influences the association between pre-pregnancy body mass index and types of delivery and birth: a comparison of urban and rural areas
Bibliographic record
Abstract
BACKGROUND: Women in study areas suffered from the problems of caesarean delivery (CD), low birth weight (LBW), and macrosomia. OBJECTIVE: To investigate how gestational weight gain (GWG) influences the effect of the pre-pregnancy body mass index (BMI) on the risks of CD, LBW, and macrosomia in urban and rural areas in a city of Iran. METHODS: We used 767 and 612 eligible subjects from the public health care centers in urban and rural areas respectively. RESULTS: The risk of CD increased from 74% to 2.62-fold in urban and from 62% to 2.15-fold in rural areas, and the risk of macrosomia increased from 58% to 2.35-fold in urban and from 47% to 96% in rural areas, among obese women compared to normal weight women who gained above median GWG. The risk of LBW increased from 38% to 92% in urban and from 49% to 97% in rural areas among lean women compared to normal weight women who gained below median GWG. CONCLUSION: These findings strongly support the need to reform adequate pre-pregnancy weight and GWG against the risks of CD and macrosomia among overweight and obese women, and against the risk of LBW among lean women in both areas.
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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 | 0.000 | 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".