Evidence map›Paper›PMID 33791213›Full record

ReviewFrontiers in oncology2021

Lymphopenia in Esophageal Cancer: What Have We Learned?

Jia-Lin Wang, Rong Ma, Wei Kong, Ren Zhao, Yan-Yang Wang

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
5.6field-weighted citation impact, top 3% 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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Lymphopenia duringEuropean journal of hybrid imaging · 2022
    Article
  9. 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

5 authors at 2 institutions in 1 country.

Jia-Lin WangDepartment of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, China.
Rong MaGraduate School, Ningxia Medical University, Yinchuan, China.
Wei KongDepartment of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, China.
Ren ZhaoDepartment of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, China.
Yan-Yang WangDepartment of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, China.
Ningxia Medical University General Hospital · CNNingxia Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lymphopenia caused by disease or treatment is frequent in patients with cancer, which seriously affects the prognosis of these patients. Immune checkpoint inhibitors (ICIs) have garnered attention as one of the most promising strategies for the treatment of esophageal cancer (EC). The status of the immune system, such as, the lymphocyte count, is now considered to be an important biomarker for ICI treatments. Recognition of the significant impact of the lymphocyte count on the survival of patients with EC in the era of immunotherapy has revived interest in understanding the causes of lymphopenia and in developing strategies to predict, prevent and eliminate the adverse effect of lymphopenia. Here, we review what we have learned about lymphopenia in EC, including the prognostic and predictive value of lymphopenia in patients with EC, the predictors of lymphopenia, and the strategies to ameliorate the effect of lymphopenia in patients with EC.

Indexed as

esophageal cancerimmunotherapylymphopeniapredictorprognosis

Identifiers

PMID33791213
PMCPMC8006429
OpenAlexW3134041527

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.