Evidence map›Paper›PMID 39157622›Full record

ArticleEJHaem2024

Identification of clinical-biological features of newly diagnosed early relapse multiple myeloma patients eligible for autologous stem cell transplantation.

Lorenzo Cillo, Anna Benedetta Dalla Palma, Stefania Ricci, Mario Pedrazzoni, Matteo Scita, Matia Bernardi, Gabriella Sammarelli, Laura Pelagatti, Nicola Giuliani

Abstract read
In one paragraph

Article in EJHaem, 2024. 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
–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

1 citing paper in PubMed.

  1. 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

9 authors.

Lorenzo CilloDepartment of Medicine and Surgery University of Parma Parma Italy.ORCID https://orcid.org/0009-0004-9570-4576
Anna Benedetta Dalla PalmaDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Stefania RicciDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Mario PedrazzoniDepartment of Medicine and Surgery University of Parma Parma Italy.
Matteo ScitaDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Matia BernardiDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Gabriella SammarelliDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Laura PelagattiDepartment of Onco-Hematology Hematology and BMT Unit University Hospital of Parma Parma Italy.
Nicola GiulianiDepartment of Medicine and Surgery University of Parma Parma Italy.ORCID https://orcid.org/0000-0003-3457-3774

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A portion of multiple myeloma (MM) patients relapse early or do not respond to first line treatment. Identification of possible clinical and or biological features of these patients remains an unmet medical need. In this study we assesed the predictive markers for early relapse MM, defined as a progressive disease that occurred within 18 months, from autologoust stem cell transplantation (ASCT) in MM patients who did not have primary refractory disease. 74 consecutive MM patients were included in the study that received intensive therapy with ASCT. The study was able to identify the main features of newly diagnosed ER MM patients eligible for ASCT identifying the IgA isotype and the R2-ISS score system as the main predictive prognostic factors for ER in this cohort of MM patients.

Indexed as

autologous stem cell transplantationearly relapsehigh riskmultiple myeloma

Identifiers

PMID39157622
PMCPMC11327756

What OpenQuestion holds

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Registered trials

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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.