Bibliometric insights into the most influential papers on antibiotic adjuvants: a comprehensive analysis
Bibliographic record
Abstract
Background: The utilization of antibiotic adjuvants presents a promising strategy for addressing bacterial resistance. Recently, the development of antibiotic adjuvants has attracted considerable attention from researchers in academia and industry. This study aimed to identify the most influential publications on antibiotic adjuvants and elucidate the hotspots and research trends in this field. Method: Original articles and reviews related to antibiotic adjuvants were retrieved from the Web of Science Core Collection database. The top 100 highly cited publications were selected and the visual analyses of publication outputs, countries, institutions, authors, journals, and keywords were conducted using Excel, VOSviewer, or CtieSpace software tools. Results: The top 100 cited publications concerning antibiotic adjuvants spanned the years 1977–2020, with citation counts ranging from 174 to 2,735. These publications encompassed 49 original articles and 51 reviews. The journal “Antimicrobial Agents and Chemotherapy” accounted for the highest number of publications (12%). The top 100 cited publications emanated from 39 countries, with the United States leading in production. Institutions in Canada and the United States exhibited the most substantial contributions to these highly cited publications. A total of 526 authors participated in these studies, with Robert E.W. Hancock, Laura J. V. Piddock, Xian-Zhi Li, Hiroshi Nikaido, and Olga Lomovskaya emerging as the most frequently nominated authors. The most common keywords included “E. coli”, “P. aeruginosa”, “S. aureus”, “in-vitro activity”, “antimicrobial peptide”, “efflux pump inhibitor” “efflux pump”, “MexAB-OprM” and “mechanism”. These keywords underscored the hotspots of bacterial resistance mechanisms and the development of novel antibiotic adjuvants. Conclusion: Through the bibliometric analysis, this study identified the top 100 highly cited publications on antibiotic adjuvants. Moreover, the findings offered a comprehensive understanding of the characteristics and frontiers in this field.
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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.206 | 0.250 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".