The 100 Top-Cited Articles in Pulmonary Imaging
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to identify and characterize the 100 top-cited articles in pulmonary imaging. MATERIALS AND METHODS: From the database of Journal Citation Reports, 274 journals whose scope included pulmonary imaging were selected. The Web of Science search tools were then used to identify the 100 top-cited articles in the subject of pulmonary imaging published in these journals. The parameters used to analyze the characteristics of the 100 top-cited articles were journal (including subject category and impact factor), publication year, number of citations and annual citations, department and institution of authors, country of origin, article type, imaging technique, and topic. RESULTS: The 100 top-cited articles in pulmonary imaging were published between 1953 and 2012, with 43 published between 2000 and 2009. Citations ranged from 199 to 1447, and annual citations ranged from 5.1 to 314. The majority of articles were published in radiology or imaging journals (n=64), originated in the United States (n=49), were original articles (n=87), used computed tomography (n=66), and were based on the topic of pulmonary thromboembolism (n=18). Department of Radiology, Mayo Clinic (n=7), and Department of Radiology, University of British Columbia and Vancouver General Hospital (n=7), were the leading institutions, and Müller NL (n=11) was the most prolific author. CONCLUSIONS: Our study lists the 100 top-cited articles in pulmonary imaging, provides an insight into historical developments, and allows for recognition of advances in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.072 | 0.079 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".