Bibliographic record
Abstract
Abstract Background: Bibliometric analysis studies are studies that examine the literature on a subject numerically and holistically, and have recently attracted a lot of attention in the field of medicine. The number of articles about pediatric caudal anesthesia has increased gradually over the past few decades. However, there is no bibliometric analysis study on pediatric caudal anesthesia in the literature. This study aimed to present a bibliometric analysis of articles published in the Web of Science (WoS) Core database related to pediatric caudal anesthesia. Method: We used the search engine of WoS and included all types of contributions (original articles, reviews, letters, etc.) in the bibliometric analysis. The keywords used to access articles are ‘’pediatric, caudal, anesthesia, analgesia, and block’’ words. For the analyses, VOSViewer 1.6.13. version was used. Results: The most cited publications on pediatric caudal anesthesia were in the following journals: Pediatric Anesthesia (84 articles, 1892 citations), Anesthesia and Analgesia (26 articles, 884 citations) and Anesthesiology (7 articles, 537 citations). The countries that publish and receive the most citations about pediatric caudal anesthesia starting from the highest are the USA, France, Sweden, Turkey, Canada. Conclusion: The following parameters were the foci of a thorough analysis of articles on pediatric caudal anesthesia: publication date, number of citations, journal name, theme, and country. It is noteworthy that pediatric caudal anesthesia currently plays a crucial role in pediatric anesthesia research. However, there is still a need for new studies from different countries on different cases in the literature on pediatric caudal anesthesia.
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 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 | 0.016 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.164 | 0.226 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".