Bibliometric profile of global scientific research on digoxin toxicity (1849–2015)
Bibliographic record
Abstract
Digoxin is a cardiac glycoside derived from the common foxglove digitalis purpurea and has been available for several centuries as a medicinal agent. Despite extensive patient experience over many years, there remains some controversy regarding the possibility that digoxin might have a deleterious effect on survival. This study was constructed to assess trends in digoxin toxicity research using well-established qualitative and quantitative bibliometric indicators. The current study is based on publications that have been indexed in Scopus. Articles referring to the subject of digoxin toxicity between 1849 and 2015 were assessed according to the document type, publication language, countries/territories, institutions, journal, impact factors, total number of citations, h-index, average number of citations per publication, and international collaborations. There were 2900 publications that included 2542 (87.7%) original research articles, while 5.3% were reviews and 4.6% letters. The country of origin was the USA in 849 publications, Germany in 241, the UK in 150, and France in 143. The USA and the UK had the highest number of international collaborations. The average number of citations per publications related to digoxin toxicity was 8.1, and the h-index was 59. The USA and Canada had the highest h-indices by country at 46 and 22, respectively. This study presents the first bibliometric analysis on digoxin toxicity publications. The USA was the most important contributors to digoxin toxicity literature with the greatest international collaboration, largest number of articles and highest h-index, followed by Germany and the UK. There has been a trend towards reduced publication numbers related to digoxin toxicity at global level, although it is still an important issue and we present the current research themes related to digoxin toxicity that were identified.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.136 | 0.213 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".