Research trends and hotspots on septic shock: a bibliometric study and visualization analysis
Bibliographic record
Abstract
Background Septic shock, the most severe stage of sepsis, causes potential circulatory failure and abnormal cell metabolism which are severe enough to affect prognosis, increase mortality, and impose significant burdens on the medical system. Despite a growing number of studies exploring the pathophysiology, epidemiology, and risk factors, research trends and hotspots in septic shock remain lacking. This study aims to create a visual knowledge map, identify research hotspots, and predict prospective trends based on bibliometric analysis. Methods We searched for publications related to septic shock in Web of Science Core Collection up to June 15, 2023. CiteSpace5.5 R2, VOS viewer and Pathfinder were used to evaluate the annual publications, countries, institutions, journals and keywords. We also analyzed the collaboration among countries, institutions and authors, and identified research hotspots and frontiers. Results A total of 4,208 English papers were included in the analysis, and the annual publication displayed a slow upward trend. In terms of publication volume, the top three countries were the United States, France, and Germany, and the University of Pittsburgh (the United States) ranked first (n = 85) among all institutions, with Jeanlouis Vincent from Erasmus Medical Center (Netherlands) as the most published author (n = 32). According to the collaborative network, the United States had the highest level of cooperation, and the University of Pittsburgh, the University of Toronto, and Columbia University were the institutions with the most foreign cooperation. Additionally, the co-author network revealed that scholars such as Jeanlouis Vincent, Rinaldo Bellomo, and Djillali Annane, had the strongest collaborations. The co-citation network showed that the top 3 most cited articles were: Singer M (2016), Rhodes A (2017), Dellinger RP (2013), and the top 3 most cited journals were Crit Care Med (3,664 times), N Engl J Med (3,207 times), Intens Care Med (3,096 times) in this field. In the keyword co-occurrence network, the most frequent keywords were “septic shock” (2531), “sepsis” (1667), and “mortality” (569), indicating the current research hotspots. Pathobiology, fluid therapy, and endotoxic septic shock were emerging trends in research. Conclusion By using bibliometrics, this study reviewed the studies in septic shock and revealed the hotspots and cutting-edge trends, including the pathogenesis of complications, the development of new biomarkers, the timing and methods of alternative treatments, and the rehabilitation trajectory, etc., which provided a reference for subsequent studies in septic shock.
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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.157 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".