Evidence map›Paper›PMID 34943824›Full record

ArticleCells2021

Combined Analysis of Early CD4

Saskia Leserer, Esteban Arrieta-Bolaños, Ulrike Buttkereit, Dietrich W Beelen, Amin T Turki

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. 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

5 authors at 1 institution in 1 country.

Saskia LesererComputational Hematology Lab, Department of Hematology and Stem Cell Transplantation, University Hospital Essen, 45122 Essen, Germany.ORCID 0000-0002-4956-1760
Esteban Arrieta-BolañosInstitute for Experimental Cellular Therapy, University Hospital Essen, 45122 Essen, Germany.ORCID 0000-0002-3696-5803
Ulrike ButtkereitDepartment of Hematology and Stem Cell Transplantation, University Hospital Essen, 45122 Essen, Germany.
Dietrich W BeelenDepartment of Hematology and Stem Cell Transplantation, University Hospital Essen, 45122 Essen, Germany.
Amin T TurkiComputational Hematology Lab, Department of Hematology and Stem Cell Transplantation, University Hospital Essen, 45122 Essen, Germany.ORCID 0000-0003-1347-3360
Essen University Hospital · DE

Funding

Deutsche Forschungsgemeinschaft FU 356/12-1Federal Ministry of Education and Research 031L0027D
6 · The paper itself

Abstract

The incidence and severity of viral complications after cellular therapy are highly variable. Recent publications describe relevant interactions between the human Cytomegalovirus (CMV) and host immunity in recipients of allogeneic hematopoietic cell transplantation (HCT). Although immune monitoring is routinely performed in HCT patients, validated cut-off levels correlating with transplant outcomes such as survival or CMV reactivation are mostly limited to day +100, which is later than the median time for CMV reactivation in the absence of medical prophylaxis. To address this gap in early risk assessment, we applied an unsupervised machine learning technique based on clustering of day +30 CD4

Indexed as

Hematopoietic Stem Cell TransplantationRisk AssessmentAdolescentAdultAgedCD4-Positive T-LymphocytesCytomegalovirusCytomegalovirus InfectionsFemaleHumansIncidenceLymphocyte CountMaleMiddle AgedPrognosisTransplantation, HomologousCD4+ T helper cellsclusteringCMV reactivationCMV risk assessmentCMV serostatusimmune reconstitutionR+ serostatusT cell reconstitutionunsupervised learning

Identifiers

PMID34943824
PMCPMC8699729
OpenAlexW3215079799

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.