A bibliometric study on the impact of gut microbiota on the efficacy of immune checkpoint inhibitors in cancer patients: analysis of the top 100 cited articles
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) have transformed oncological treatment by modulating immune responses against tumors. However, their efficacy is subject to inter-patient variability and is associated with immune-related adverse events (irAEs). The human gut microbiota, a complex microbial ecosystem, is increasingly implicated in modulating responses to ICIs. This bibliometric analysis examines the 100 most-cited articles to elucidate trends and advancements in research concerning the gut microbiota's impact on ICI efficacy. Methods: A systematic literature retrieval was conducted within the Web of Science Core Collection (WoSCC), focusing on the 100 most-cited articles. VOSviewer and CiteSpace were utilized for bibliometric analysis, examining collaborative patterns and keyword co-occurrences. The relationship between citing and cited entities was analyzed, and burst ranking identified research hotspots based on citation frequency. Results: The 100 most-cited publications encompassed a range of disciplines, with a predominance of oncological research. The United States and China were leading in publication volume, with France and Canada also contributing significantly. French institutions, particularly INSERM and Université Paris Cite, were prolific. Routy, Bertrand and Zitvogel, Laurence were prominent among high-impact authors. Dominant keywords included "gut microbiota," "immunotherapy," "efficacy," and "cancer." The article by Routy et al. (2018) was the most frequently cited. Conclusions: This study highlights the significant role of the gut microbiota in ICI development and efficacy, emphasizing the necessity for international and interdisciplinary collaboration. The research is progressively focusing on managing immunotherapy side effects and optimizing treatment strategies. Challenges, including individual variability in gut microbiota composition, persist. Further research is imperative to exploit the potential of the gut microbiota in cancer therapy, advocating for personalized approaches and a more profound comprehension of the underlying mechanisms.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.151 | 0.198 |
| 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) | 0.004 | 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".