Evidence map›Paper›PMID 42410814›Full record

ArticleMedicine2026

Effect of evidence-based predictive nursing on postoperative infection and recovery outcomes in cesarean delivery: A case-control study.

Sun Xuehua, Zhang Yan, Yu Huan

Abstract read
In one paragraph

Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Sun XuehuaDepartment of Obstetrics and Gynecology, Shijiazhuang Obstetrics and Gynecology Hospital, Hebei, China.
Zhang YanDepartment of Obstetrics and Gynecology, Shijiazhuang Obstetrics and Gynecology Hospital, Hebei, China.ORCID 0009-0009-2532-3045
Yu HuanDepartment of Obstetrics, Handan Maternal and Child Health Hospital, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cesarean section (CS) is associated with a high risk of postoperative infection, which can compromise maternal recovery and increase the healthcare burden. Conventional nursing models often lack individualized risk assessment and proactive strategies. This study aimed to evaluate the effectiveness of evidence-based predictive nursing in reducing postoperative infection and improving recovery among women undergoing CS. A case-control study was conducted between March 2023 and June 2024, enrolling 240 women who underwent cesarean section. Participants were divided into 2 groups according to the nursing care received: the control group (routine nursing care, n = 120) and the intervention group (evidence-based predictive nursing, n = 120). The intervention involved preoperative risk assessment, individualized health education, intraoperative aseptic management, and postoperative monitoring of inflammatory markers (C-reactive protein, white blood cell). The primary outcome was postoperative infection within 7 days, with secondary outcomes comprising hospital stay, inflammatory marker trends, and patient satisfaction. The incidence of postoperative infection was significantly lower in the intervention group than in the control group (2.0% vs 8.0%, P = .015). Intervention patients had shorter hospital stays (5.3 ± 1.1 vs 6.4 ± 1.3 days, P < .001), faster normalization of C-reactive protein and white blood cell levels (interaction P < .05), and higher satisfaction scores (91.8 ± 4.7 vs 85.2 ± 6.1, P < .001). Multivariable analysis confirmed the intervention as an independent protective factor (OR = 0.35, 95% CI: 0.15-0.82). Incorporating evidence-based predictive nursing into risk models improved discrimination for infection (area under the curve 0.85 vs 0.71; P < .01). Evidence-based predictive nursing significantly reduces postoperative infection and enhances recovery in CS patients. This strategy is safe, effective, and holds potential for broader implementation. Future multicenter studies should validate these findings and assess cost-effectiveness.

Indexed as

Cesarean SectionEvidence-Based NursingPostoperative ComplicationsSurgical Wound InfectionAdultCase-Control StudiesC-Reactive ProteinFemaleHumansLength of StayPatient SatisfactionPregnancyRisk AssessmentC-Reactive Proteincesarean sectionevidence-based nursinginflammatory markersnursing quality improvementpostoperative infectionpredictive nursing

Identifiers

PMID42410814
PMCPMC13337019

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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.