Evidence map›Paper›PMID 41282983›Full record

ArticlePeerJ2025

Risk factors and predictive model for intrapartum cesarean delivery in women with epidural analgesia: a retrospective cohort study.

Yu Feng, Yili Zhao, Rui Shao, Yan Wang, Pin Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yu Feng *Department of Anesthesiology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Yili Zhao *Legal Supervision Department, Plastic and Cosmetic Surgery Department, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Rui ShaoCentral Operating Department, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Yan WangDepartment of Anesthesiology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Pin ZhangDepartment of Anesthesiology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Labor epidural analgesia (LEA) is the gold standard for labor pain management, yet its association with intrapartum cesarean delivery (CD) remains controversial. This study aimed to identify risk factors and develop a predictive model for CD conversion in parturients receiving LEA. Methods: A retrospective cohort study analyzed 1,896 parturients receiving LEA at Zhengzhou Central Hospital (2022-2023). Participants were categorized into vaginal delivery group ( Results: The CD conversion rate was 18.7%. Eight independent predictors were identified: height, pre-pregnancy body mass index (BMI), gestational age ≥ 40 weeks, hypertension, meperidine use, cervical dilation at analgesia initiation, intrapartum fever (≥ 38.0 °C), and estimated fetal weight. The nomogram demonstrated good discrimination (training set area under the curve (AUC) = 0.729, 95% confidence interval (CI) [0.695-0.763]; testing set AUC = 0.722, 95% CI [0.670-0.774]) and calibration (Hosmer-Lemeshow Conclusions: This model provides a tool for individualized labor management that aims to reduce unnecessary cesarean deliveries.

Indexed as

Analgesia, EpiduralAnalgesia, ObstetricalCesarean SectionAdultChinaFemaleHumansNomogramsPregnancyRetrospective StudiesRisk FactorsROC CurveEpidural analgesiaIntrapartum cesarean deliveryLabor analgesiaRisk factors

Identifiers

PMID41282983
PMCPMC12640134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.