- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2046-4053
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Statistical stopping criteria for automated screening in systematic reviews ; volume:9 ; number:1 ; day:28 ; month:11 ; year:2020 ; pages:1-14 ; date:12.2020
Systematic Reviews ; 9, Heft 1 (28.11.2020), 1-14, 12.2020
- Creator
-
Callaghan, Max W.
Müller-Hansen, Finn
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s13643-020-01521-4
- URN
-
urn:nbn:de:101:1-2021010818352910707977
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:34 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Callaghan, Max W.
- Müller-Hansen, Finn
- SpringerLink (Online service)
Other Objects (12)
Stopping criteria for nonlinear variational problems: iterative approach
Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research
Systematic and other reviews: criteria and complexities
Statistical software computing reviews: MICROFIT 3.21
Statistical software computing reviews: MICROFIT 3.21
Smoothed residual stopping for statistical inverse problems via truncated SVD estimation
Evaluation of the performance criteria of optimal futility stopping boundaries in flexible designs
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Computer-assisted screening in systematic evidence synthesis requires robust and well-evaluated stopping criteria
Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint
Optimal stopping rules
Stopping criteria for nonlinear variational problems: iterative approach
Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research
Systematic and other reviews: criteria and complexities
Statistical software computing reviews: MICROFIT 3.21
Statistical software computing reviews: MICROFIT 3.21
Smoothed residual stopping for statistical inverse problems via truncated SVD estimation
Evaluation of the performance criteria of optimal futility stopping boundaries in flexible designs
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Computer-assisted screening in systematic evidence synthesis requires robust and well-evaluated stopping criteria
Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint
Optimal stopping rules
Stopping criteria for nonlinear variational problems: iterative approach
Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research
Systematic and other reviews: criteria and complexities
Statistical software computing reviews: MICROFIT 3.21
Statistical software computing reviews: MICROFIT 3.21
Smoothed residual stopping for statistical inverse problems via truncated SVD estimation
Evaluation of the performance criteria of optimal futility stopping boundaries in flexible designs
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
Computer-assisted screening in systematic evidence synthesis requires robust and well-evaluated stopping criteria
Optimality criteria for futility stopping boundaries for group sequential designs with a continuous endpoint