Evidence map›Paper›PMID 36059646›Full record

ArticleFrontiers in oncology2022

Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.

Adrián Mosquera Orgueira, Andrés Peleteiro Raíndo, José Ángel Díaz Arias, Beatriz Antelo Rodríguez, Mónica López Riñón, Claudio Cerchione, Adolfo de la Fuente Burguera, Marta Sonia González Pérez, Giovanni Martinelli, Pau Montesinos Fernández and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Adrián Mosquera OrgueiraDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
Andrés Peleteiro RaíndoDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
José Ángel Díaz AriasDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
Beatriz Antelo RodríguezDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
Mónica López RiñónDepartment of Hematology, Tomelloso Hospital, Ciudad Real, Spain.
Claudio CerchioneUnit of Hematology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "DinoAmadori", Meldola, Italy.
Adolfo de la Fuente BurgueraDepartment of Hematology, MD Anderson Cancer Center, Madrid, Spain.
Marta Sonia González PérezDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.
Giovanni MartinelliUnit of Hematology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "DinoAmadori", Meldola, Italy.
Pau Montesinos FernándezDepartment of Hematology, Hospital La Fe, Valencia, Spain.
Manuel Mateo Pérez EncinasDepartment of Hematology, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as

Indexed as

leukemiamachine learningpredictionrisksurvivaltranscriptome

Identifiers

PMID36059646
PMCPMC9428690

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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.