Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.
- DOI
- 10.1186/s12874-023-02103-3
- Published
- 2023 Nov 24
- Container
- BMC medical research methodology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s12874-023-02103-3,
title = {Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.},
author = {Ledger A and Ceusters J and Valentin L and Testa A and Van Holsbeke C and Franchi D and Bourne T and Froyman W and Timmerman D and Van Calster B},
year = {2023},
journal = {BMC medical research methodology},
doi = {10.1186/s12874-023-02103-3},
url = {https://doi.org/10.1186/s12874-023-02103-3}
}RIS
TY - JOUR TI - Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm. AU - Ledger A AU - Ceusters J AU - Valentin L AU - Testa A AU - Van Holsbeke C AU - Franchi D AU - Bourne T AU - Froyman W AU - Timmerman D AU - Van Calster B PY - 2023 JO - BMC medical research methodology DO - 10.1186/s12874-023-02103-3 UR - https://doi.org/10.1186/s12874-023-02103-3 ER -
APA
A, L., J, C., L, V., A, T., C, V. H., D, F., T, B., W, F., D, T., & B, V. C. (2023). Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.. BMC medical research methodology. https://doi.org/10.1186/s12874-023-02103-3
Source records
- pubmed · retrieved 2026-09-27T11:26:10.526Z