Cesarean Delivery Among Nulliparous Women in Beirut: Assessing Predictors in Nine Hospitals
Bibliographic record
Abstract
BACKGROUND: Obstetric practice has witnessed a worldwide trend of increasing cesarean section rates in recent years. Similar trends have been observed in Lebanon, according to 2 studies conducted in 1996 and 1999. The objective of the present study was to assess the differences in predictors of cesarean delivery among nulliparous women in a "control hospital" with a low cesarean delivery rate (12.5%) and the rest of the National Collaborative Perinatal Neonatal Network (NCPNN) "study hospitals" with a higher cesarean delivery rate (31.4%). METHODS: Data were collected by the NCPNN database, which covers deliveries at 9 major hospitals located in the Greater Beirut area. Data analysis was performed on the 6,668 consecutive deliveries occurring between January 1, 2001, and December 31, 2002, at the NCPNN participating centers. The questionnaires included items that cover parental sociodemographic characteristics and maternal and newborn health characteristics. Sources of data included direct interviews with mothers after delivery and before hospital discharge and reviews of obstetric and nursery medical charts. Chi-square tests and t tests were performed for categorical and continuous clinical predictors of cesarean section. Logistic regression was performed to determine the odds of having a cesarean section for the study hospitals when compared with the control hospital. Odds ratios and 95% confidence intervals are reported. RESULTS: Variables in the study hospitals that correlated with a higher cesarean delivery rate were male obstetricians, day of the week, and mode of payment compared with the control hospital. CONCLUSIONS: In a country with a high cesarean section rate, 1 hospital met World Health Organization criteria for acceptable cesarean section rates, with no compromise in neonatal outcome. Further studies are needed to investigate potential policies to decrease the high cesarean section rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".