Evidence map›Paper›PMID 37564928›Full record

ArticleFrontiers in oncology2023

Involvement in treatment decision-making and self-reported efficacy among patients with advanced colorectal cancer: a nationwide multi-center cross-sectional study.

Xiao-Fen Gu, Hui-Fang Xu, Yin Liu, Li Li, Yan-Qin Yu, Xi Zhang, Xiao-Hui Wang, Wen-Jun Wang, Ling-Bin Du, Shuang-Xia Duan and 12 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Rationalizing the Influence of Co-Design on Distress, Clinical Decision-Making and Disease Self-Management of Cancer Patients-as-Partners: A Quasi-Experimental Study.Health expectations : an international journal of public participation in health care and health policy · 2024
    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

22 authors.

Xiao-Fen GuDepartment of Student Affairs, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.
Hui-Fang XuDepartment of Cancer Epidemiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China.
Yin LiuDepartment of Cancer Epidemiology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, China.
Li LiDepartment of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Yan-Qin YuThe Clinical Epidemiology of Research Center, Department of Public Health and Preventive Medicine, Baotou Medical College, Baotou, China.
Xi ZhangKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital & Institute, Beijing, China.
Xiao-Hui WangDepartment of Public Health, Gansu Provincial Cancer Hospital, Lanzhou, China.
Wen-Jun WangSchool of Nursing, Jining Medical University, Jining, China.
Ling-Bin DuDepartment of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China.
Shuang-Xia DuanDepartment of Preventive Health, Xinxiang Central Hospital, Xinxiang, China.
He-Lu CaoDepartment of Preventive Health, Xinxiang Central Hospital, Xinxiang, China.
Yu-Qian ZhaoCenter for Cancer Prevention Research, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yun-Yong LiuLiaoning Office for Cancer Control and Research, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China.
Juan-Xiu HuangDepartment of Gastrodiges, Wuzhou Red Cross Hospital, Wuzhou, China.
Ji CaoDepartment of Cancer Prevention and Control Office, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yan-Ping FanState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Chang-Yan FengChongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China.
Xue-Mei LianSchool of Public Health and Management, Chongqing Medical University, Chongqing, China.
Jing-Chang DuSchool of Public Health, Chengdu Medical College, Chengdu, China.
Remila RezhakeDepartment of Student Affairs, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.
Li MaPublic Health School, Dalian Medical University, Dalian, China.
You-Lin QiaoDepartment of Student Affairs, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This cross-sectional study evaluated the involvement of patients with advanced colorectal cancer (CRC) in treatment decision-making, assessed the treatment efficacy according to their self-reports, and investigated the influencing factors. Methods: Patients with advanced CRC were recruited from 19 hospitals from March 2020 to March 2021 by a multi-stage multi-level sampling method. A self-designed questionnaire was used to collect demographic and clinical characteristics, involvement of CRC patients in treatment decision-making, treatment methods, and self-reported efficacy. Univariate and unordered multinomial logistic regression analyses were used to evaluate the factors affecting the involvement in treatment decision-making and self-reported efficacy. Results: We enrolled 4533 patients with advanced CRC. The average age at diagnosis was 58.7 ± 11.8 years. For the treatment method, 32.4% of patients received surgery combined with chemotherapy, 13.1% of patients underwent surgery combined with chemotherapy and targeted therapy, and 9.7% of patients were treated with surgery alone. For treatment decision-making, 7.0% of patients were solely responsible for decision-making, 47.0% of patients shared treatment decision-making with family members, 19.0% of patients had family members solely responsible for treatment decision-making, and 27.0% of patients had their physicians solely responsible for treatment decision-making. Gender, age, education level, family income, marital status, treatment cost, hospital type, and treatment method were significantly associated with the involvement of patients in treatment decision-making. A total of 3824 patients submitted self-reported efficacy evaluations during treatment. The percentage of patients with good self-reported efficacy was 76.5% (for patients treated for the first time), 61.7% (for patients treated for the second time), and 43.2% (for patients treated after recurrence and metastasis), respectively. Occupation, education level, average annual family income, place of residence, time since cancer diagnosis, hospital type, clinical stage, targeted therapy, and involvement in treatment decision-making were the main influencing factors of self-reported efficacy of treatment. Discussion: Conclusively, CRC patients are not highly dominant in treatment decision-making and more likely to make treatment decisions with their family and doctors. Timely and effective communication between doctors and patients can bolster patient involvement in treatment decision-making.

Indexed as

Chinacolorectal cancerdecision makingself-reported efficacytreatment

Identifiers

PMID37564928
PMCPMC10411882

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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.