Evidence map›Paper›PMID 32695166›Full record

ReviewJournal of oncology2020

Precision Detection Technology: Equipping Precision Oncology with Wings.

Rilan Bai, Zheng Lv, Xiao Chen, Hanfei Guo, Ling Bai, Huimin Tian, Wei Li, Jiuwei Cui

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 47% of its field
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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
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

8 authors at 1 institution in 1 country.

Rilan BaiCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Zheng LvCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Xiao ChenCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Hanfei GuoCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Ling BaiCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Huimin TianCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.
Wei LiCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.ORCID https://orcid.org/0000-0002-9479-028X
Jiuwei CuiCancer Center, The First Hospital of Jilin University, Changchun, Jilin 130021, China.ORCID https://orcid.org/0000-0001-6496-7550
Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, precision medical detection techniques experienced a rapid transformation from low-throughput to high-throughput genomic sequencing, from multicell promiscuous detection to single-cell precision sequencing. The emergence of liquid biopsy technology has compensated for the many limitations of tissue biopsy, leading to a tremendous transformation in precision detection. Precision detection techniques contribute to monitoring disease development more closely, evaluating therapeutic effects more scientifically, and developing new targets and new drugs. In the future, the role of precision detection and the joint detection in epigenetics, rare gene detection, individualized targeted therapy, and multigene targeted drug combination therapy should be extensively explored. This article reviews the changes in precision medical detection technology in the era of precision medicine, as well as the development, clinical application, and future challenges of liquid biopsy.

Identifiers

PMID32695166
PMCPMC7368234
OpenAlexW3040899714

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.