To analyze the development trend of the ultrasound-guide nerve block basing the bibliometric analysis
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.113 | 0.159 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".