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Toward assessing clinical trial publications for reporting transparency
Halil Kilicoglu
, Graciela Rosemblat
, Linh Hoang
, Sahil Wadhwa
, Zeshan Peng
, Mario Malički
,
Jodi Schneider
, Gerben ter Riet
School of Information Sciences
National Center for Supercomputing Applications (NCSA)
Carl R. Woese Institute for Genomic Biology
Beckman Institute for Advanced Science and Technology
Coordinated Science Lab
European Union Center
Informatics
Nutritional Sciences
Research output
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peer-review
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Keyphrases
Clinical Trials
100%
Randomized Controlled Trial
100%
Trial Publication
100%
Reporting Transparency
100%
Text Mining
33%
Agreement Measure
33%
Set-valued
33%
Mining Model
33%
Text Mining Techniques
33%
Krippendorff
33%
BioBERT
33%
Peer Review
16%
Reliability Method
16%
Publicly Available
16%
Item-level
16%
Support Vector Machine
16%
Majority Vote
16%
Annotator
16%
Sentence-level
16%
Large Corpora
16%
Annotation Scheme
16%
Phrase-based
16%
Inter-annotator Agreement
16%
Annotated Corpus
16%
Rule-based Method
16%
Supervised Learning Method
16%
Vote Aggregation
16%
Reporting Guidelines
16%
Neural Network Classifier
16%
Methods Section
16%
Support Method
16%
Trial Method
16%
Label Aggregation
16%
Trial Appraisal
16%
CONSORT Checklist
16%
Combining Models
16%
Arts and Humanities
Clinical
100%
Controlled
100%
Corpus
100%
On-set
33%
Peer Review
16%
Majority
16%
Scheme
16%
Authoring
16%
Rigor
16%
Computer Science
Controlled Trial
100%
Text Mining
66%
Learning Approach
16%
Neural Network
16%
Annotation
16%
Testbed
16%
Support Vector Machine
16%
Supervised Learning
16%
Header Section
16%
Social Sciences
Randomized Controlled Trial
100%
Legal Procedure
100%
Peer Review
16%
Median
16%
Inter-annotator Agreement
16%
Neural Network
16%
Earth and Planetary Sciences
Supervised Learning
100%
Support Vector Machine
100%
Psychology
Text Mining
100%
Neural Network
25%