Evidence map›Paper›PMID 38459168›Full record

ArticleLeukemia2024

Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells.

Margot F van Spronsen, Sofie Van Gassen, Carolien Duetz, Theresia M Westers, Yvan Saeys, Arjan A van de Loosdrecht

Open access · hybridAbstract read
In one paragraph

Article in Leukemia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 6 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

6 authors at 3 institutions in 2 countries.

Margot F van SpronsenDepartment of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Centre Amsterdam, Amsterdam, Netherlands.
Sofie Van GassenVIB Inflammation Research Centre, Ghent University, Ghent, Belgium.ORCID 0000-0002-7119-5330
Carolien DuetzDepartment of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Centre Amsterdam, Amsterdam, Netherlands.
Theresia M WestersDepartment of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Centre Amsterdam, Amsterdam, Netherlands.
Yvan SaeysVIB Inflammation Research Centre, Ghent University, Ghent, Belgium.
Arjan A van de LoosdrechtDepartment of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Centre Amsterdam, Amsterdam, Netherlands. a.vandeloosdrecht@amsterdamumc.nl.ORCID 0000-0001-8311-983X
Amsterdam University Medical Centers · NLGhent University · BEAmsterdam Neuroscience · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes. To contribute to the risk stratification of MDS by immunophenotypic profiles, this study performed computational clustering of flow cytometry data of CD34

Indexed as

Hematopoietic Stem CellsMyelodysplastic SyndromesAdultAgedAged, 80 and overAntigens, CD34Case-Control StudiesCluster AnalysisFemaleFlow CytometryHumansImmunophenotypingLeukemia, Myeloid, AcuteMaleMiddle AgedPrognosisAntigens, CD34

Identifiers

PMID38459168
PMCPMC11147773
OpenAlexW4392581846

What OpenQuestion holds

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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.