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Identifying Bridge Symptoms Linking Physical and Psychological Burdens in Postoperative Pancreatic Cancer Patients: A Network Analysis.

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Psycho-oncology 📖 저널 OA 58.2% 2026 Vol.35(2) p. e70400
Retraction 확인
출처

Song Y, Jin S, Li L, Li Z, Liu Y, Liu J, Zhuang S

📝 환자 설명용 한 줄

[BACKGROUND] Pain, fatigue, psychological distress, and fear of progression often co-occur in postoperative pancreatic cancer patients.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 연구 설계 cross-sectional

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↓ .bib ↓ .ris
APA Song Y, Jin S, et al. (2026). Identifying Bridge Symptoms Linking Physical and Psychological Burdens in Postoperative Pancreatic Cancer Patients: A Network Analysis.. Psycho-oncology, 35(2), e70400. https://doi.org/10.1002/pon.70400
MLA Song Y, et al.. "Identifying Bridge Symptoms Linking Physical and Psychological Burdens in Postoperative Pancreatic Cancer Patients: A Network Analysis.." Psycho-oncology, vol. 35, no. 2, 2026, pp. e70400.
PMID 41665518
DOI 10.1002/pon.70400

Abstract

[BACKGROUND] Pain, fatigue, psychological distress, and fear of progression often co-occur in postoperative pancreatic cancer patients. The coexistence and interaction of these symptoms can further exacerbate burden. Existing research has mainly focused on single symptom or broad symptom clusters, leaving the interaction structure and key symptoms insufficiently understood.

[AIMS] To characterize correlations among these symptoms in postoperative pancreatic cancer patients, and to identify the central and bridge symptoms most suitable as intervention targets through network analysis.

[METHODS] This multicenter cross-sectional study included 512 postoperative pancreatic cancer patients. Symptom data were collected using the Global Pain Scale, Cancer Fatigue Scale, Distress Management Screening Measure, and Fear of Progression Questionnaire. Network analysis was conducted to map symptom correlations, with central and bridge symptoms identified using the qgraph and networktools packages. The bootnet package was employed to assess model stability.

[RESULTS] The symptom network showed substantial overall connectivity. Two strongest positive edges were P1 (pain intensity) - D6 (distress thermometer) and F1 (physical fatigue) - FoP5 (peer-relationship strain). P1 (pain intensity) had the greatest expected influence value, followed by D6 (distress thermometer), indicating central symptoms. Moreover, D6 (distress thermometer), F1 (physical fatigue), P1 (pain intensity), and FoP5 (peer-relationship strain) were found as bridges linking different communities. The network demonstrated good stability in case-dropping bootstrap analyses.

[CONCLUSION] Targeted interventions directed at the identified central and bridge symptoms may help reduce overall symptom burden and improve quality of life. These findings provide a foundation for developing more refined and individualized symptom management strategies.

🏷️ 키워드 / MeSH

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