Journal metrics, document network, and conceptual and social structures of the Korean Journal of Anesthesiology from 2017 to July 2022: a bibliometric study
Bibliographic record
Abstract
BACKGROUND: This study aimed to identify the directions of research published in the Korean Journal of Anesthesiology (KJA) and identify the main topics and journal network through a bibliometric analysis. The results can be reflected in strategies for the journal's promotion to a top-ranking journal in the anesthesiology category. METHODS: KJA articles from January 1, 2017 to September 11, 2022 were retrieved from the Web of Science Core Collection on September 11, 2022, and analyzed using Biblioshiny. Journal metrics, the document network, the conceptual structure, and social structures were elucidated. RESULTS: Out of 525 articles, fewer than half (48.6%) were from Korean corresponding authors. The impact factor steeply increased from 2.316 in 2019 to 5.167 in 2021. The Hirsch index of KJA was 24. A co-occurrence network of Keywords Plus showed four clusters of central keywords: surgery, management, anesthesia, and mortality. The conceptual structure map of Keywords Plus showed a main cluster of anesthesia and analgesia, while another minor cluster included intubation and induction. The co-citation network demonstrated that KJA was in the same cluster of anesthesiology journals. The collaboration network of the authors' countries showed that Korean authors collaborated mainly with researchers in the United States and Canada. CONCLUSIONS: These results show KJA's developmental process of promotion to a top-tier journal in the anesthesiology category. Furthermore, the following strategies are suggested for journal promotion: recruitment of articles on emerging and highly citable topics; and more active collaboration of society members with researchers worldwide.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.071 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".