MétaCan
Menu
Back to cohort

To analyze the development trend of the ultrasound-guide nerve block basing the bibliometric analysis

2017· article· en· W3029739230 on OpenAlexaboutno aff
Lei-Ming Weng

Bibliographic record

VenueZhonghua yixue keyan guanli zazhi · 2017
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBibliometricsUltrasoundTrend analysisStage (stratigraphy)Field (mathematics)Citation analysisMedicineComputer scienceLibrary scienceCitationRadiologyMathematics

Abstract

fetched live from OpenAlex

Objective To study the development trend and research status of the subject in the field of the development trend of ultrasound-guided nerve by bibliometric analysis. Methods By using PubMed and SCI-E databases, we searched the literatures about ultrasound-guided nerve published over these years, and used the GoPubMed platform and BIBLIOMETRC.COM to document the bibliometric data from two sources separately. Results Through a series of comparative analysis, we discussed the research status and development trend of ultrasound-guided nerve , obtained the core research force in this field, and summarized the periodicals and hot-spots of this article. Conclusions Ultrasound-guided nerve block has gradually entered the slow stage of research in the world, but the related research in our country is still in the rising stage. At present, the leading force in this field is still in the United States and Canada. We should pay more attention to track the trends and improve our research capabilities. Key words: Ultrasound; Nerve block bibliometric analysis; PubMed; SCI-E; Bibliometric analysis

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.034
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.366
Teacher spread0.319 · 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; a candidate call from one teacher head, not a consensus.

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueZhonghua yixue keyan guanli zazhiSame topicMedical Research and TreatmentsFrench-language works237,207