Data Cut-off Rules
Data Cut-Off Rules is a formal methodological framework that specifies predetermined conditions or thresholds for excluding or truncating data points from a dataset based on systematic criteria such as time, quality, relevance, or measurement boundaries.
47
Publications
4.6K
Citations
733
Authors
363
Institutions
Publications per year
2017–2026
35
Variants
Data Cut-off Criteria
Parents
47
Tony Mok, D. Ross Camidge, Shirish M. Gadgeel et al. · Annals of Oncology · 2020 · 686 citations · Full text
Medicine, Oncology, Pharmacotherapy +12
1–5 of 47
733
3
h-index
University of North Carolina at Chapel Hill, United States
3 publications · 422 citations
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 733
363
Massachusetts General Hospital
Boston, United States
6 h-index
8 publications
3K citations

University of Colorado Denver
Denver, United States
5 h-index
7 publications
2.5K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
5 publications
1.4K citations
Memorial Sloan Kettering Cancer Center
New York, United States
4 h-index
8 publications
1.6K citations
Pfizer (United States)
New York, United States
4 h-index
12 publications
2.4K citations
University of California, San Francisco
San Francisco, United States
3 h-index
4 publications
766 citations
Roche (Switzerland)
Basel, Switzerland
3 h-index
27 publications
3.8K citations
Kanagawa Cancer Center
Yokohama, Japan
3 h-index
4 publications
739 citations
1–8 of 363
4 publications · 447 citations
4
h-index
3 publications · 1.6K citations
3
h-index
2 publications · 281 citations
2
h-index
MSJ · 2 publications · 94 citations
2
h-index
1 publications · 24 citations
1
h-index
1–5