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Record W4291377793 · doi:10.2147/jpr.s372303

Regional Anesthesia (2012–2021): A Comprehensive Examination Based on Bibliometric Analyses of Hotpots, Knowledge Structure and Intellectual Dynamics

2022· review· en· W4291377793 on OpenAlexaboutno aff
Abdullah M. Shbeer

Bibliographic record

VenueJournal of Pain Research · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScopusProductivityBibliometricsLibrary scienceMEDLINEEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Abstract: In the last decade, there has been a significant advancement in the area of regional anesthesia (RA). Continuous evaluation of research in any developing field using modern technologies and available software is critical to identify future trends, hot spots, and intellectual dynamics. The current study was designed to bibliometrically evaluate the global research in RA using VOSviewer, MS Excel, and CVS-Scopus bibliographic data (2012– 2021). Knowledge structure and intellectual dynamics were analyzed using clustering of keyword co-occurrence. Literature screening in the last decade found 6092 original articles (96.1%) and conference papers (3.9%). The top four countries producing articles were the United States (n = 30.57%), India (7.51), the United Kingdom (7.22%), and Canada (6.06%). A significant positive correlation was found in global publication productivity (R 2 = 0.9161). The most productive organizations were Harvard University, the University of Toronto, and the Hospital for Special Surgery – New York. A tremendous collaboration was spotted nationally and internationally, especially in pediatric RA. This comprehensive study, which summarizes and evaluates 6902 original research materials on regional anesthesia, may serve as a resource for anesthesiologists, physicians, researchers, and students. Keywords: regional anesthesia, bibliometrics, VOSviewer, knowledge structure, intellectual dynamics

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)0.008
metaresearch head score (Gemma)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0450.029
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.286
GPT teacher head0.482
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pain ResearchSame topicCardiac, Anesthesia and Surgical OutcomesFrench-language works237,207