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Record W2945263934 · doi:10.1111/petr.13455

Publication trends in pediatric renal transplantation: Bibliometric analysis of literature from 1950 to 2017

2019· article· en· W2945263934 on OpenAlexaff
Mandy Rickard, Jessica H. Hannick, Nicolás Fernández, Martin A. Koyle, Krista MacMurdo, Armando J. Lorenzo

Bibliographic record

VenuePediatric Transplantation · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsMarkham Stouffville HospitalSickKids FoundationHospital for Sick ChildrenMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineImpact factorBibliometricsTransplantationScience Citation IndexCitationIndex (typography)Observational studyMEDLINERetrospective cohort studyCitation analysisInternal medicineDemographyLibrary science

Abstract

fetched live from OpenAlex

INTRODUCTION: Pediatric renal transplantation has been heavily published since the 1950s. Herein, we describe the bibliometrics and impact of the 200 most-cited pediatric renal transplantation manuscripts. METHODS: We identified pediatric renal transplantation publications from 1900 onwards. Year, citations, h-index, geographic origin, impact factor, topic, and design of the 200 top-cited papers were extracted. Impact index was calculated, adjusting for citation volume and time since publication. RESULTS: Of the top 200 papers, mean citation count was 80 ± 40, impact factor 3.9 ± 3.7, h-index 35 ± 20, and impact index 25 ± 13. Studies were mostly retrospective (31%) or observational (32%). Most papers originated from the United States (58%), Germany (9%), and Italy (6%), which did not correlate with citation counts. Transplantation (18%), Pediatric Nephrology (16%), and American Journal of Transplantation (11%) had the highest publication volume, which did not correlate with citation count. The main topics were medical renal disease, drug monitoring, compliance, and viruses. Most of the top-cited papers (179; 90%) were published after 1991. The difference in the number of times cited between papers published before and after 1991 was insignificant (75 ± 24 vs 80 ± 42; P = 0.59). There was a difference in impact index for the same period (48 ± 15 vs 22 ± 10; P < 0.01). CONCLUSIONS: The most-cited papers were concentrated in three journals, but the top three cited papers were published elsewhere. Recent publications were more cited with a higher impact than older papers. Despite the importance of surgery in transplantation, there is a paucity of high-impact papers on this topic.

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
Observationalhigh
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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication, 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 score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.7960.927
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.189
GPT teacher head0.475
Teacher spread0.285 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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