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Record W3107982530 · doi:10.3389/fpsyg.2020.550716

The 100 Top-Cited Studies on Neuropsychology: A Bibliometric Analysis

2020· article· en· W3107982530 on OpenAlexaboutno aff
Yang Zhang, Ying Xiong, Yujia Cai, Linli Zheng, Yonggang Zhang

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer-related cognitive impairment studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeuropsychologyWeb of scienceImpact factorBibliometricsPsychologyClinical neuropsychologyLibrary scienceMeta-analysisPsychiatryMedicinePolitical scienceComputer scienceCognition

Abstract

fetched live from OpenAlex

Objective The present study aimed to identify and analyze the bibliometric characteristics of the 100 top-cited studies on neuropsychology. Methods We searched the Web of Science Core Collection database to collect studies on neuropsychology from inception to 31st December 2019. Two authors independently screened the literature and extracted the data. Statistical analyses were performed using R software. Results The 100 top-cited articles were cited a total of 166,123 times, ranging from 736 to 24,252 times per article. All of the studies were published from 1967 to 2014 in 47 journals. Neuropsychologia had the highest number of articles ( n = 17), followed by Neurology ( n = 8). The top three most productive countries were the USA ( n = 60), England ( n = 13), and Canada ( n = 8). Eight authors contributed the same number of studies as the first author ( n = 2) or corresponding author ( n = 2). The most productive institute was the University of California ( n = 9), followed by the University of Pennsylvania ( n = 4). Of the 100 top-cited publications, 64 were original articles, and 36 were reviews. The top three Web of Science categories were clinical neurology ( n = 28), behavioral sciences ( n = 19), and psychiatry ( n = 11). Conclusion This study provides insight into the impact of neuropsychology research and may help doctors, researchers, and stakeholders to achieve a more comprehensive understanding of trends and most influential contributions to the field, thus promoting ideas for future investigation.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0280.132
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.392
Teacher spread0.336 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

Explore more

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