MétaCan
Menu
Back to cohort
Record W3122632720 · doi:10.2174/1874303x02013010039

Top 100 Cited Articles on Peritoneal Dialysis: A Bibliometric Analysis

2020· article· en· W3122632720 on OpenAlexaboutno aff
Yoo Jin Lee, Bong Soo Park, Jin Han Park, Kang Min Park, Il Hwan Kim, Junghae Ko, Sihyung Park, Yang Wook Kim

Bibliographic record

VenueThe Open Urology & Nephrology Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsPeritoneal dialysisMedicineBibliometricsNephrologyInternal medicineSubject (documents)DialysisFamily medicineLibrary science

Abstract

fetched live from OpenAlex

Background: The purpose of this study is to broaden the understanding of peritoneal dialysis by presenting the most-cited articles pertaining to this subject. Methods: We searched articles on the Web of Science and selected 100 articles according to the frequency of citations. Next, we reviewed the contents of the articles and identified the characteristics of these articles. Results: There are 21 journals in which the top-100 cited articles were published. The names of the journals, in the order in which the articles are cited, are as follows: Kidney International (25 articles), Nephrology Dialysis Transplantation (13 articles), and Journal of the American Society of Nephrology (12 articles). The top 100-cited articles were published in 15 countries. The country with the greatest number of publications was the United States of America (19 articles). The institution with the greatest number of articles (7 articles) was the University Health Network of Toronto. The author who wrote the most number of articles (5 articles) was Davies SJ. Conclusion: This study is the first in the field of nephrology to provide a list of the top-100 cited articles dedicated to peritoneal dialysis. Through this study, the research trends and major academic interests pertaining to peritoneal dialysis would be identified.

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 armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0170.064
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.040
GPT teacher head0.310
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe Open Urology & Nephrology JournalSame topicDialysis and Renal Disease ManagementCategoryBibliometricsFrench-language works237,207