ArticleJournal of the American College of Radiology : JACR2015
Code Abdomen: An Assessment Coding Scheme for Abdominal Imaging Findings Possibly Representing Cancer.
Article in Journal of the American College of Radiology : JACR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Automated Detection of Cancer-Suspicious Findings in Japanese Radiology Reports with Natural Language Processing: A Multicenter Study.Journal of imaging informatics in medicine · 2025Article
- Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.European radiology · 2021Article
- Musculoskeletal Outside Interpretation (MOI-RADS): an automated quality assurance tool to prospectively track discrepancies in second-opinion interpretations in musculoskeletal imaging.Skeletal radiology · 2021Article
- Automatic Fully-Contextualized Recommendation Extraction from Radiology Reports.Journal of digital imaging · 2021Article
- Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.Journal of digital imaging · 2020Article
- Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.Journal of digital imaging · 2018Article
- Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.Journal of digital imaging · 2017Article
- Expanding the Scope of an Automated Radiology Recommendation-Tracking Engine: Initial Experiences and Lessons Learned.Journal of digital imaging · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.