Reasons for elective cesarean section on maternal request: a systematic review
Bibliographic record
Abstract
Background: Given the increasing rate of cesarean delivery and request without maternal or fetal indication among pregnant women, this systematic review was conducted to obtain the reasons for maternal request for elective cesarean section.Methods: We searched published studies from the first year of records through August 2018 in PubMed, Scopus, and Web of Science. The quality assessment of the studies was performed by the improved Newcastle-Ottawa Scale. Due to data heterogeneity; no meta-analysis was performed.Results: Twenty-eight studies met the inclusion criteria and were included in the review. The results of studies on the reasons of maternal request for elective cesarean section were fear of labor pain, anxiety for fetal injury/death, fear of childbirth, urinary incontinence, pelvic floor and vaginal trauma, doctors suggestion, time of birth, experience of prior bad delivery, previous infertility, infertility, anxiety for gynecologic examination, anxiety for loss of control, avoid long labor, anxiety for lack of support from the staff, fear of fecal, emotional aspects, body weight of the infant at birth and abnormal prenatal examination. The results of studies on the demographic reasons of maternal request for elective cesarean section were advanced maternal age, parity, occupation, education, maternal obesity, family status, decreasing level of religiosity, household income, number of living children and age at marriage.Conclusions: Our study proposed that the comprehensive programs and the interventions of health promotion should be designed to reduce unnecessary cesarean section and improve the performance of vaginal delivery.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".