Contribution of Turkey in Heart Transplant Research: A Web of Science Database Search
Bibliographic record
Abstract
OBJECTIVES: In 2001, Turkey performed its the first successful heart transplant. Since 2011, 765 heart transplants have been conducted among 15 heart transplant centers. The scientific impact of Turkish articles on heart transplantation remains uncertain. The purpose of this study was to evaluate Turkey's contributions in international heart transplant research. MATERIALS AND METHODS: The bibliometric study approach was used to assess publications on heart transplantation, which included analysis of year of publication, organizations/authors, sponsorship, keywords, citations, and other characteristics. Titles, abstracts, and key words were searched in the Web of Science database for terms that included "heart" or "cardiac" and "transplantation." Methods for both quantitative and qualitative data analysis were used. RESULTS: During the analysis period of 1970 through 2021, 6370 article publications were retrieved with an average of 20.88 citations/article and 133 018 total citations. H index was 129. Most of the retrieved articles were from research areas of surgery (n = 2876; 45.14%), followed by transplantation (n = 2818; 44.23%) and cardiovascular system cardiology (n = 2522; 39.59%). Annual citation growth showed slow growth until 1986. The highest number of citations was seen in 2021 (n = 702). The United States led countries on articles (n = 2924; 45.9%), followed by Germany (n = 458; 7.19%), England (n = 411; 6.45%), Canada (n = 384; 6.02%), France (n = 330; 5.18%), and Spain (n = 329; 5.16%). The other 84 countries totaled 753 (11.82%) articles. Turkey ranked eighteenth with 87 publications, with Başkent University (n = 37) and Ege University (n = 13) being the leading centers on heart transplant research in Turkey. CONCLUSIONS: Publications from the United States continue to increase. The workload of both transplant surgery and research and publishing is challenging and Turkish researchers are encouraged to make strides at innovations.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.054 | 0.072 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".