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TP53 in AML and MDS: The new (old) kid on the block

Marks, Jennifer A., Wang, Xin, Fenu, Elena M., Bagg, Adam, Lai, Catherine·2023·Blood Reviews

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Cited by

See all 20

Metadata

Authors
Marks, J. A., Wang, X., Fenu, E. M., Bagg, A., & Lai, C.
Venue
Blood Reviews
Publisher
Elsevier BV
Published
2023-07
Volume
60
Pages
101055
ISSN
0268-960X
Cited by
20
References
31
Language
en

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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This page
CoChat. (2026). Citation check: TP53 in AML and MDS: The new (old) kid on the block. https://app.cochat.ai/papers/10.1016/j.blre.2023.101055
The paper
Marks, J. A., Wang, X., Fenu, E. M., Bagg, A., & Lai, C. (2023). TP53 in AML and MDS: The new (old) kid on the block. Blood Reviews. https://doi.org/10.1016/j.blre.2023.101055

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.