A bibliometric analysis of the 100 most influential papers on peritoneal dialysis
Bibliographic record
Abstract
BACKGROUND: We aimed to identify the 100 most cited articles published on peritoneal dialysis (PD) and analyze their characteristics to provide information on the achievements and developments of PD research over the past decades. METHODS: The Science Citation Index Expanded (SCIE) in the Web of Science Core Collection was comprehensively searched from 2000 to 2018, using the keywords "Peritoneal dialysis" or "Dialyses, Peritoneal" or "Dialysis, Peritoneal" or "Peritoneal Dialyses". The top 100 cited articles were retrieved by reading titles and abstracts. Significant information was further elicited, including the authors, journals, countries, institutions, and publication year. RESULTS: The United States was the most productive country (n = 51), Li Pkt published the highest number of papers (n = 7), the Journal of the American Society of Nephrology produced the highest number of contributions (n = 28), and Baxter International Inc., the University of California System, and the University of Toronto were the institutions with the highest number of articles (n = 10). CONCLUSIONS: This is the first bibliometric study to identify the most influential papers in PD research. This report describes the major changes and advances in research regarding PD as a guide for writing a citable article.
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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.188 | 0.208 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".