Bibliometric analysis on clinical research of lung transplantation from PubMed database
Bibliographic record
Abstract
Objective To analyze the status quo of clinical lung transplantation worldwide by an overview of related literatures from PubMed.Methods Papers were retrieved in PubMed database and were analyzed by using bibliometric tools such as Thomson Data Analyzer (TDA) and UCINET. The existing state of literatures of lung transplantation was analyzed in terms of distributions of years, countries, institutions and journals, and high-frequency subject headings were identified using co-word analysis.Results The literature of clinical lung transplantation was traced back to 1966 and began to increase in 1988. The United States had leading advantages in the field of lung transplantation. The numbers of papers from United Kingdom, Denmark, France, Germany and Canada were on the top 6. The development of lung transplantation research in China was relatively slow. Out of the top 30 institutions, 17 were in the United States, accounting for 56.67%. No core institution was located in china. Of the top 20 authors, 5 were from the United Kingdom and 9 from the United States. The journal published the most papers of lung transplantation was the Journal of Heart and Lung Transplantation. The high-frequency subject headings included graft rejection, postoperative complications, immunosuppressive agents, and bronchiolitis obliterans. Conclusion The increase of clinical lung transplant literatures has been relatively slow since its emergence 40 years ago. The United States and the United Kingdom are the top 2 countries in this field. Donor and recipient selection, management of postoperative complications, and long-term survival of recipients are the hot topics in clinical lung transplantation.
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.198 | 0.221 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".