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Bibliometric analysis of international pediatric clinical journal PEDIATRICS

2013· article· en· W3032061478 on OpenAlexaboutno aff
XU Wen-jie

Bibliographic record

VenueZhonghua shiyong erke linchuang zazhi · 2013
Typearticle
Languageen
FieldMedicine
TopicHuman Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsBibliometricsMedicinePediatricsNinthWeb of scienceMEDLINEFamily medicineLibrary scienceMeta-analysisPolitical sciencePathology

Abstract

fetched live from OpenAlex

Objective To introduce the international pediatric clinical journal PEDIATRICS for pediatrics clinical medical researchers. Methods Articles of PEDIATRICS from Jan.2008 to Oct.2012 were searched through Web of Science database.Analysis in country, language, authors and frequency of subject headings based on bibliometrics methodologies was performed. Results In recent 5 years, articles of PEDIATRICS involved a total of 96 countries or regions.The United States published the most papers, 2687 of the total amount of literatures, accounted for 64.89%; followed by Canada, 295 of the literatures, accounted for 7.12%.China ranked ninth, a total of 73 papers of literature, accounted for 1.76%.Article was the most frequent type of the papers and English was the dominating language. Conclusions Currently, the researches of PEDIATRICS focus in the fields of epidemiology, care, management, influence factors, diagnosis, treatment, mortality statistics, asthma, obesity. Key words: PEDIATRICS; Bibliometrics; Journal research

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 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)0.003
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 score0.032
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0760.083
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.046
GPT teacher head0.383
Teacher spread0.337 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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