ReviewLeukemia2018
Diagnostic algorithm for lower-risk myelodysplastic syndromes.
Review in Leukemia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- Prognostic value of flow cytometry in myelodysplastic neoplasms (MDS): Composition of a FCM-prognostic score (FCM-PS) for overall survival.HemaSphere · 2026Article
- A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.Scientific data · 2025Article
- MDS-Type Morphologic Abnormalities of Peripheral Blood Granulocytes in Symptomatic COVID-19 Patients.International journal of hematology-oncology and stem cell research · 2024Article
- Potential Diagnostic Value of Abnormal Pyroptosis Genes Expression in Myelodysplastic Syndromes (MDS): A Primary Observational Cohort Study.International journal of hematology-oncology and stem cell research · 2024Article
- Review
- Significant improvement of bone marrow-derived MSC expansion from MDS patients by defined xeno-free medium.Stem cell research & therapy · 2023Article
- Management of patients with lower-risk myelodysplastic syndromes.Blood cancer journal · 2022Review
- Article
- Guidelines for Myelodysplastic Syndromes: Converting Evidence into Action?International journal of environmental research and public health · 2021Review
- A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid advances over the past decade have uncovered the heterogeneous genomic and immunologic landscape of myelodysplastic syndromes (MDS). This has led to notable improvements in the accuracy and timing of diagnosis and prognostication of MDS, as well as the identification of possible novel targets for therapeutic intervention. For the practicing clinician, however, this increase in genomic, epigenomic, and immunologic knowledge needs consideration in a "real-world" context to aid diagnostic specificity. Although the 2016 revision to the World Health Organization classification for MDS is comprehensive and timely, certain limitations still exist for day-to-day clinical practice. In this review, we describe an up-to-date diagnostic approach to patients with suspected lower-risk MDS, including hypoplastic MDS, and demonstrate the requirement for an "integrated" diagnostic approach. Moreover, in the era of rapid access to massive parallel sequencing platforms for mutational screening, we suggest which patients should undergo such analyses, when such screening should be performed, and how those data should be interpreted. This is particularly relevant given the recent findings describing age-related clonal hematopoiesis.
Indexed as
Identifiers
29946191What OpenQuestion holds
Registered trials
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.