Bibliographic record
Abstract
The study's goal is to examine the scientific outputs on Trachoma that have been published globally. A descriptive bibliometric analysis study was carried out. The Web of Science Core Collection was used as a bibliographic database and VOSviewer software version 1.6.18 for Windows was used to create the required network visualization. The search was conducted by using the keywords "trachoma" or "Chlamydia trachomatis" in the title. The most extensive timeframe was used, which included the years 1970 through 2021. Other publication genres such as case reports, editorials, and letters were eliminated from the search since they were not peer-reviewed papers. The overall citation counts of each trachoma-related publication published was the study's primary outcome. The topic of the publications, the publishing journal, and the year published, the language, the place of origin, the names of the first authors, the Hirsch (H) indexes, and the number of citations analyzed were all secondary outcomes. A total of 6556 articles were detected. The number of articles has never dropped under 100 articles per year since 1985. The highest number of articles was published in 2021 (n=233). 6251 (95.348%) of the articles were published in Science Citation Index Expanded (SCI-Expanded) journals. The University of California System was the leading affiliation on trachoma research. The USA (n=2585), England (n=910), and Canada (n=336) were the countries with the higher number of publications. The articles from the USA had the highest H indexes and the articles from England had a higher number of average citations per item. Studies on trachoma are increasing worldwide. The USA and England are the leading countries in scientific production in this regard. The USA and England are the leading countries in scientific production on this topic.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.038 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".