Exploring Global Research Status and Trends in Necrotizing Fasciitis: A Bibliometric Analysis
Bibliographic record
Abstract
Necrotizing fasciitis (NF) is a rare and extremely destructive soft tissue infection characterized by rapid progression and severe clinical manifestations. It can lead to serious complications and even death without prompt diagnosis and treatment. Despite advances in medical science, NF remains a major clinical challenge. This study analyzed 3381 NF-related research papers from the Web of Science Core Collection (WoSCC) database (1977-2024). We used text mining and bibliometric methods to research the number of papers, publishing institutions and countries, key authors, research hotspots, evolving trends, and other information, and to visualize the results. The analysis revealed a significant increase in publications, from fewer than 10 per year before 1990 to 228 in 2023. The United States, China, and Canada were the leading contributors, with the United States forming a core research network with Canada and the UK, while Chang Gung University in Taiwan, China, emerged as a key research hub in Asia. Early studies primarily focused on pathogens, whereas recent research has shifted toward treatment techniques and outcome prediction. High-frequency keywords like "mortality" and "diagnosis" reflect ongoing clinical challenges. Key unresolved issues include the diagnostic accuracy of the LRINEC score, optimal debridement timing, and health care disparities in resource-limited regions. The findings indicate a shift in NF research focus from etiology to clinical management and technological innovation. Future research should aim to refine diagnostic criteria, explore personalized treatments, and improve diagnostic capabilities in underserved areas. Interdisciplinary approaches, including information technology and materials science, are expected to drive NF research toward precision medicine.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.117 | 0.191 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".