Evidence map›Paper›PMID 37655186›Full record

ReviewGlobal health & medicine2023

Current status of and future perspectives on care for cancer survivors in China.

Jie Song, Ruijia Li, Xiaojing Hu, Gang Ding, Minxing Chen, Chunlin Jin

Abstract readReview
In one paragraph

Review in Global health & medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
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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

6 authors.

Jie SongShanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, China.
Ruijia LiShanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, China.
Xiaojing HuShanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, China.
Gang DingOncology Department, Shanghai International Medical Center, Shanghai, China.
Minxing ChenShanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, China.
Chunlin JinShanghai Health Development Research Center, Shanghai Medical Information Center, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is currently a major public health issue faced by countries around the world. With the progress of medical science and technology, the survival rate of cancer patients has increased significantly and the survival time has been effectively prolonged. How to provide quality and efficient care for the increasingly large group of cancer survivors with limited medical resources will be a key concern in the field of global public health in the future. Compared to developed countries, China's theoretical research and practical experience in care for cancer survivors are relatively limited and cannot meet the multi-faceted and diverse care needs of cancer patients. Based on the existing models of care worldwide, the current work reviews care for cancer survivors in China, it proposes considerations and suggestions for the creation of models of cancer care with Chinese characteristics in terms of optimizing top-level system design, enhancing institutional mechanisms, accelerating human resource development, and enhancing self-management and social support for patients.

Indexed as

cancer survivorsChinamodel of carequality of lifesurvivorship

Identifiers

PMID37655186
PMCPMC10461331

What OpenQuestion holds

Textmetadata
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.