Bibliometric analysis of global gonorrhea research
Bibliographic record
Abstract
OBJECTIVE: This study offers a systematic and comprehensive picture of the field, which researchers may use to assess the characteristics of articles involving Neisseria gonorrhoeae. MATERIALS AND METHODS: In the current bibliometric analysis study, the Web of Science (WoS; formerly Web of Knowledge) database was used to accomplish the objective of the study. The keywords “Neisseria gonorrhoeae” “gonorrhoeae” or “N. gonorrhoeae” or “gonorrhea” were used in the search, using “title” as the search item. The article category was referred to as the document type in this study. RESULTS: In the WOS database, 4,250 articles were retrieved for the entire study period. Most of the articles were published between the years 2010-2019 (27.506%). The articles were from 84 different study areas. Most of them from Microbiology (n= 1829, 43.035%), Infectious Diseases (n=1664, 39.153%) and Immunology (n=793, 18.659%) areas. The top-ranked country was the United State of America (USA) (n=1931, 45.435%) in this field. Also, England, Canada, Sweeden, and Australia were the most productive countries on this topic. Turkey ranked 46th. 4,250 articles were cited 106,469 times, H index average per item was 117. The number of citations increased over the years. CONCLUSIONS: Our findings revealed that current research on the subject of gonorrhea has increased dramatically, as expected, and has covered a wide range of specialties. With a focus on the USA, American and European institutions are by far the most influential regions of the globe in terms of study 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 | 0.018 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.192 | 0.243 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".