Top 50 Highly Cited Articles on Dual Energy Computed Tomography (DECT) in Abdominal Radiology: A Bibliometric Analysis
Bibliographic record
Abstract
This study aims to identify the 50 most highly cited articles on dual energy computed tomography (DECT) in abdominal radiology. Thomson Reuters Web of Science All Databases was queried without year or language restriction. Only original research articles with a primary focus on abdominal radiology using DECT were selected. Review articles, meta-analyses, and studies without human subjects were excluded. Fifty articles with the highest average yearly citation were identified. These articles were published between 2007 and 2017 in 12 journals, with the most in Radiology (12 articles). Articles had a median of 7 authors, with all first authors but one primarily affiliated to radiology departments. The United States of America produced the most articles (16), followed by Germany (13 articles), and China (7 articles). Most studies used Dual Source DECT technology (35 articles), followed by Rapid Kilovoltage Switching (14 articles), and Sequential Scanning (1 article). The top three scanned organs were the liver (24%), kidney (16%), and urinary tract (15%). The most commonly studied pathology was urinary calculi (28%), renal lesion/tumor (23%), and hepatic lesion/tumor (20%). Our study identifies intellectual milestones in the applications of DECT in abdominal radiology. The diversity of the articles reflects on the characteristics and quality of the most influential publications related to DECT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.129 | 0.137 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".