Evidence map›Paper›PMID 29113330›Full record

ArticleOncotarget2017

Flow cytometric analysis of CD64 expression pattern and density in the diagnosis of acute promyelocytic leukemia: a multi-center study in Shanghai, China.

Min Liu, Xiangqin Weng, Shenglan Gong, Hui Chen, Jing Ding, Mengqiao Guo, Xiaoxia Hu, Jianmin Wang, Jianmin Yang, Gusheng Tang

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 22 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Min Liu *Department of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Xiangqin Weng *Institute of Hematology, Ruijin Hospital, Jiaotong University, Shanghai, China.
Shenglan GongDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Hui ChenDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Jing DingDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Mengqiao GuoDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Xiaoxia HuDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Jianmin WangDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Jianmin YangDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Gusheng TangDepartment of Hematology, Changhai Hospital, The Second Military Medical University, Shanghai, China.
Changhai Hospital · CNSecond Military Medical University · CNRuijin Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

No unified immunophenotypic profiles and corresponding analytic strategies have been established for the rapid diagnosis of acute promyelocytic leukemia (APL) using flow cytometry (FCM). Here we describe a characteristic immunophenotypic panel that can rapidly and accurately distinguish APL from other types of adult acute myeloid leukemia (AML) using only FCM. By comparing APL cells and non-APL AML cells that share APL common immunophenotypes (CD34

Indexed as

acute promyelocytic leukemia (APL)APL diagnostic immunophenotypic paneldiagnostic performanceflow cytometry

Identifiers

PMID29113330
PMCPMC5655225
OpenAlexW2756126591

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.