Top 100 cited articles in cardiovascular magnetic resonance: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: With limited health care resources, bibliometric studies can help guide researchers and research funding agencies towards areas where reallocation or increase in research activity is warranted. Bibliometric analyses have been published in many specialties and sub-specialties but our literature search did not reveal a bibliometric analysis on Cardiovascular Magnetic Resonance (CMR). The main objective of the study was to identify the trends of the top 100 cited articles on CMR research. METHODS: Web of Science (WOS) search was used to create a database of all English language scientific journals. This search was then cross-referenced with a similar search term query of Scopus® to identify articles that may have been missed on the initial search. Articles were ranked by citation count and screened by two independent reviewers. RESULTS: Citations for the top 100 articles ranged from 178 to 1925 with a median of 319.5. Only 17 articles were cited more than 500 times, and the vast majority (n = 72) were cited between 200-499 times. More than half of the articles (n = 52) were from the United States of America, and more than one quarter (n = 21) from the United Kingdom. More than four fifth (n = 86) of the articles were published between the time period 2000-2014 with only 1 article published before 1990. Circulation and Journal of the American College of Cardiology made up more than half (n = 62) of the list. We found 10 authors who had greater than 5 publications in the list. CONCLUSION: Our study provides an insight on the characteristics and quality of the most highly cited CMR literature, and a list of the most influential references related to CMR.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.173 | 0.177 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".