Evidence map›Paper›PMID 35338671›Full record

ArticleAmerican journal of hematology2022

Integration of multiparameter flow cytometry score improves prognostic stratification provided by standard models in primary myelofibrosis.

Francesco Mannelli, Sara Bencini, Giacomo Coltro, Giuseppe G Loscocco, Benedetta Peruzzi, Giada Rotunno, Chiara Maccari, Francesca Gesullo, Miriam Borella, Chiara Paoli and 5 more

Open access · hybridAbstract read
In one paragraph

Article in American journal of hematology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

10 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Prognostic and Predictive Models in Myelofibrosis.Current hematologic malignancy reports · 2024
    Review
  4. Article
  5. Review
  6. Myelofibrosis.Blood · 2023
    Review
  7. Trend of circulating CD34British journal of haematology · 2023
    Article
  8. Article
  9. Review
  10. 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

15 authors at 2 institutions in 1 country.

Francesco MannelliCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.ORCID 0000-0003-4810-6501
Sara BenciniCentro Diagnostico di Citofluorimetria e Immunoterapia, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Florence, Italy.
Giacomo ColtroCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Giuseppe G LoscoccoCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.ORCID 0000-0002-6241-1206
Benedetta PeruzziCentro Diagnostico di Citofluorimetria e Immunoterapia, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Florence, Italy.
Giada RotunnoCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Chiara MaccariCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Francesca GesulloCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Miriam BorellaCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Chiara PaoliCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Roberto CaporaleCentro Diagnostico di Citofluorimetria e Immunoterapia, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Florence, Italy.
Carmela MannarelliCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Francesco AnnunziatoCentro Diagnostico di Citofluorimetria e Immunoterapia, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Florence, Italy.
Paola GuglielmelliCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.
Alessandro M VannucchiCRIMM, Centro di Ricerca e Innovazione per le Malattie Mieloproliferative, Azienda Ospedaliera Universitaria Careggi, Dipartimento di Medicina Sperimentale e Clinica, Denothe Excellence Center, Università degli Studi, Florence, Italy.ORCID 0000-0001-5755-0730
Azienda Ospedaliero-Universitaria Careggi · ITUniversity of Siena · IT

Funding

Cancer Research UK
6 · The paper itself

Abstract

Prognostic modeling in myelofibrosis (MF) has classically pursued the integration of informative clinical and hematological parameters to separate patients' categories with different outcomes. Modern stratification includes also genetic data from karyotype and mutations. However, some poorly standardized variables, as peripheral blood (PB) blast count by morphology, are still included. In this study, we used multiparameter flow cytometry (MFC) with the aim of improving performance of existing scores. We studied 363 MF patients with available MFC files for PB CD34+ cells count determination at diagnosis. We adapted Ogata score to MF context including 2 parameters: absolute CD34+ cells count (/μL) and granulocytes to lymphocytes SSC ratio. A score of 1 was attributed to above-threshold values of each parameter. Accordingly, patients were categorized as MFC

Indexed as

Primary MyelofibrosisAntigens, CD34Flow CytometryHumansMutationPrognosisAntigens, CD34

Identifiers

PMID35338671
PMCPMC9682857
OpenAlexW4221039767

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.