Reducing the risk of low or high birth weight for women with low or high body mass index under the care of high quality hospital by using instrumental variable
Bibliographic record
Abstract
Background and purpose: Women with low (high) pre-pregnancy body mass index (BMI) recently delivered infants with approximately normal or close to normal birth weights under the high quality of prenatal care.This study estimated the effect of pre-pregnancy BMI when concerns about the effects of different quality levels of prenatal care and the health status of mothers and their infants existed. Materials and Methods:The sample consisted of the female patients who referred to one of the two hospitals with different quality levels of prenatal care in Gorgan.The logistic mixed effect model and Chi-square test did not show any significant effect of low (high) BMI on the risk of low (high) birth weight.Then, the two-stage residual inclusion instrumental variable (IV) method was used to estimate the effect of BMI in order to overcome the effects of the levels of quality care and the health status of the mothers and their infants.Results: Adjusted IV analysis revealed that women with a low BMI experienced an approximately 18% (RR=0.82;95% CI (0.69, 0.97)) reduction in the risk of delivering a LBW infant and women with a high BMI experienced an approximately 26% (RR=0.74;95% CI (0.57, 0.96)) reduction in the risk of delivering a HBW infant when they were under the care of a high quality hospital.Conclusion: This study revealed that the effect of BMI is confounded by the effects of quality of care and the health status of the mothers and their infants.Further, these results contributed to providing the conditions in improving the health status of mothers and their infants during pregnancy in local 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".