Urban-rural differences in the pregnancy-related adverse outcome
Bibliographic record
Abstract
Abstract Background Little is known about potential urban-rural differences in adverse pregnancy outcomes. The purpose of this study is to look into the urban-rural differences in the trend of adverse maternal and neonatal outcomes. Methods We retrospectively assessed the pregnancy outcome of singleton pregnant mothers who gave birth at a tertiary hospital in Bandar Abbas, Iran, between January 1st, 2020, and January 1st, 2022. Mothers were divided into two groups based on living residency: 1) urban groupand 2) rural group.Demographic factors, obstetrical factors, maternal comorbidities, and adverse maternal and neonatal outcomeswere extracted from the electronic data of each mother. The Chi-square testwas used to compare differences between the groups for categorical variables. Logistic regression models were used to assess the association of adverse pregnancy, childbirth, and neonatal outcome with living residency. Results Of 8888 mothers that gave birth during the study period, 2989 (33.6%) lived in rural areas. Adolescent pregnancy was more common in the rural area. Urban mothers had a higher education than rural mothers. Rural mothers were at higher risk for preterm birth aOR 1.81 (CI:1.24-2.99), post-term pregnancy aOR 1.5 (CI: 1.07-2.78), anemia aOR 2.02 (CI:1.07-2.34), low birth weight (LBW) aOR 1.89 (CI: 1.56-2.11), need for neonatal resuscitation aOR 2.66 (CI: 1.78-3.14), and neonatal intensive care unit (NICU) admission aOR 1.98 (CI:1.34-2.79). On the other hand, the risk of cesarean section was significantly lower compared to urban mothers aOR 0.58 (CI: 0.34-0.99). Conclusions Our study discovered that mothers living in rural areas had a higher risk of developing anemia, preterm birth, post-term pregnancies, LBW, need for neonatal resuscitation, and NICU admission, but a lower risk of cesarean section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".