Home-Based Self-Management After Permanent Pacemaker Implantation: What Should Patients Know
Bibliographic record
Abstract
Han Yan,1,* Yu Shuang Chen,2,* Yi Li,2 Guan-Xing Wei,2 Fang Ma,3 Qiu Lan Hu,4 Lan Ding,5 Wei Wei,6 Yan Li,7 Yang-Juan Bai2 1Intensive Care Unit of Cardiac Surgery Department, QingDao Municipal Hospital (Group), Qingdao, Shandong, People’s Republic of China; 2The First Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 3Nursing Department, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4Geriatric Intensive Care Unit, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 5Outpatient Department, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 6Department of Gastrointestinal Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 7Department of Obstetrics, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yang-Juan Bai, The First Department of Cardiology, The First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, Yunnan, 650032, People’s Republic of China, Tel +8613678757051, Email baiyangjuan300325@126.comObjective: The purpose of this study is to systematically explore and summarize the best evidence of home-based self-management in patients with permanent pacemaker implantation, providing comprehensive references and guidance for improving the self-management ability of such patients.Methods: Using the “ 6S” model, a systematic literature search was conducted on home-based self-management in patients with permanent pacemaker implantation. The types of literature included best practice, clinical decision-making, guidelines, expert consensus, systematic review and evidence summary. According to the inclusion and exclusion criteria, two researchers screened the literature, evaluated the quality, extracted the data, synthesized the evidence.Data Sources: BMJ Best Practice, Up To Date, Joanna Briggs Institute (JBI), the World Health Organization (WHO), the Guidelines International Network (GIN), the National Institute for Health and Care Excellence (NICE), the Registered Nurses Association of Ontario (RNAO), the Scottish Intercollegiate Guidelines Network (SIGN), the American Heart Association (AHA), the Cochrane Library, PubMed, CINAHL, Web of Science, Medlive, CNKI Database, SinoMed (China Biomedical Literature Database), Wanfang Database and VIP Database. The retrieval period spanned from the establishment of these databases until January 31, 2025.Results: A total of 21 articles were included, 1 best practice, 9 clinical decision-making, 5 guidelines, 3 expert consensus, 1 systematic review, and 2 evidence summaries. Thirty-five pieces of the best evidence from 7 aspects were summarized, including complication management, electromagnetic interference management, sports rehabilitation management, operative limb management, postoperative follow-up management, role management, and emotional management.Conclusion: This review comprehensively summarized the best evidence home-based self-management in patients with permanent pacemaker implantation. These findings are scientific and referential, and provide evidence-based support for nursing and management of patients during home-based rehabilitation.Keywords: permanent pacemaker, self-management, best evidence, evidence-based nursing
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".