The 20 most-cited articles in echocardiography literature
Bibliographic record
Abstract
A Bibliometric Analysis discussed by the six authors With the incidence of heart failure and valvular heart disease on the rise,1–2 it has been speculated that the role of non-invasive cardiac imaging especially echocardiography and cardiovascular magnetic resonance imaging will become even more pivotal.3 Transthoracic echocardiographic (TTE) imaging is the most commonly performed non-invasive cardiac diagnostic tool, with an estimated 25 million studies performed annually worldwide.4 Considering the substantial amount of clinical and research data on echocardiography in the medical literature, it is an uphill task for a clinician to address the large amount of resources in an efficient, timely, and accurate manner. The diversity of procedures, their relevance to cardiac pathology and their diagnostic and therapeutic significance is reflected by a similar heterogeneity of the data on cardiac imaging and the journals that publish it. As we move into the future of evidence-based medicine, the organization of this information becomes increasingly crucial to enable the detection of gaps and weaknesses in these repositories of knowledge. Such data can help to enhance research productivity by facilitating research allocation and rationalizing research organizations.5
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.088 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".