Evidence map›Paper›PMID 39050110›Full record

ReviewAsia-Pacific journal of oncology nursing2024

Efficacy and results of virtual nursing intervention for cancer patients: A systematic review and meta-analysis.

Hui Fang, Yajun Sun, Dongfeng Yu, Yuhong Xu

Abstract readReview
In one paragraph

Review in Asia-Pacific journal of oncology nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. PanSupport: a community model of cancer supportive care provision.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025
    Article
  4. Article
  5. 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

4 authors.

Hui FangDepartment of Oncology, Hangzhou Cancer Hospital, Hangzhou, China.
Yajun SunDepartment of Oncology, Hangzhou Cancer Hospital, Hangzhou, China.
Dongfeng YuDepartment of Oncology, Hangzhou Cancer Hospital, Hangzhou, China.
Yuhong XuDepartment of Oncology, Hangzhou Cancer Hospital, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Virtual nursing interventions, which use virtual reality and artificial intelligence technology to provide remote care for patients, have become increasingly common in cancer treatment, especially during the COVID-19 pandemic. This study was to evaluate the efficacy of virtual nursing interventions for cancer patients in contrast to conventional, in-person care. Methods: Eight randomized controlled trials (RCTs) contrasted virtual nursing with conventional techniques that satisfied the inclusion criteria were found after a thorough search across databases including PubMed, Web of Science, CINAHL, EMBASE, the Cochrane Library, Scopus, and APA PsycINFO. RevMan 5.3 software was utilized for data analysis after the included literature's quality was assessed and the intended consequence indicators were extracted. Results: Virtual nurse interventions enhanced the quality of life of cancer patients (standardized mean difference [SMD] = 0.22, 95% confidence interval [CI] 0.01 to 0.43, Conclusions: All things considered, virtual nursing shows potential as an adjunctive element of all-inclusive cancer care delivery models, deserving of further investigation and thoughtful incorporation into healthcare systems.

Indexed as

Cancer careMeta-analysisVirtual nursing

Identifiers

PMID39050110
PMCPMC11267009

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.