Determinants of prematurity in urban Indonesia: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Indonesia is the fifth country with the highest number of preterm births worldwide. More than a third of neonatal deaths in Indonesia were attributed to preterm birth. Residential areas affected the occurrence of preterm birth due to differing socioeconomic and environmental conditions. Many studies have investigated the determinants of prematurity in Indonesia, however, most of them were performed in rural areas. This study is the first meta-analysis describing the determinants of preterm birth in urban Indonesia, which aimed to become the foundation upon implementing the most suitable preventative measure and policy to reduce the rate of preterm birth. METHODS: We collected all published papers investigating the determinants of preterm birth in urban Indonesia from PubMed MEDLINE and EMBASE, using keywords developed from the following key concepts: "preterm birth", "determinants", "risk factors", "Indonesia" and the risk factors, such as "high-risk pregnancy", "anemia", "pre-eclampsia", and "infections". Exclusion criteria were multicenter studies that did not perform a specific analysis on the Indonesian population or did not separate urban and rural populations in their analysis, and articles not available in English or Indonesian. The Newcastle Ottawa Scale was used to assess the risk of bias. This systematic review was registered in PROSPERO. RESULTS: Sixteen articles were included in the analysis and classified into five categories: genetic factors, nutrition, smoking, pregnancy characteristics or complications, and disease-related characteristics. CONCLUSIONS: Our meta-analysis revealed adolescent pregnancy, smoking, eclampsia, bacterial vaginosis, LC-PUFA, placental vitamin D, and several minerals as the significant determinants of preterm birth in urban Indonesia.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".