Maternal and Neonatal Factors Influencing Preterm Birth and Low Birth Weight in Oman: A Hospital Based Study
Bibliographic record
Abstract
Background: Preterm births (PTB) and low birth weight (LBW) - the two distinct adverse pregnancy outcomes - are the major determinants of perinatal survival and development. The purpose of this study was to determine the incidence of LBW and PTB and identify the maternal and neonatal risk factors influencing them. Methods: Data for the study come from a cross-sectional retrospective study conducted at the maternity ward of Sultan Qaboos University Hospital (SQUH) in Oman during the period between November 2011 and February 2012. Data on 534 singleton live births that occurred during the study period were extracted from hospital record. Descriptive statistics, bivariate analysis and multivariate logistic regression model were used for data analysis. Results: The incidence of PTB and LBW were observed to be 9.7% and 13.7% respectively. Half (51.8%) of the LBW babies were PTB and 48.2% of the LBW babies were of term births. Differences and similarities were noted for the risk profile for PTB and LBW. Risk factors specific to PTB were maternal age, previous pregnancy loss, and infant’s length, while birth interval, maternal weight and BMI during pregnancy, and gestational age were the risk factors unique to LBW. ANC visit, infant’s gender, Apgar score, and head circumference of infants were the common significant risk factors influencing both LBW and PTB. Conclusions: The incidence of PTB and LBW are moderately high in Oman. They are associated with different risk factors. A greater understanding and modification of identified risk factors would help reduce the incidence of PTB and LBW in Oman.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".