Research priorities and trends in pulmonary tuberculosis in Latin America: A bibliometric analysis
Bibliographic record
Abstract
Tuberculosis (TB) poses a significant global public health challenge, particularly in developing countries. Over the years, scientific research has played a pivotal role in addressing this disease. In this study, we aimed to analyze and outline the trends in scientific output on TB and identify research priorities in Latin America (LA) from 1990 to 2021. Scientific production was analyzed, and the number of publications, financing sources, and journal characteristics were evaluated. Collaboration networks and keywords were visualized using mapping analysis with VOSviewer software. Research themes were prioritized by country based on co-occurrence frequency. In total, 4399 documents were identified, a significant trend was evident in the number of publications per year (R 2 = 0.981), and research substantially contributed to the reduction of TB-related mortality (R 2 = −0.876). Most publications were original articles (83.8 %). The International Journal of Tuberculosis and Lung Disease had the highest publication and citation rates per document. International collaboration was predominantly with the United States, France, and Canada. Brazil, Argentina, and Mexico had the highest number of publications and external collaborations. In LA, interest in researching studies related to treatment and diagnosis (32.5 %) was notably high, followed by epidemiology and screening (26.9 %). Among the 20 countries in LA, research priorities varied, with the highest emphasis on HIV/AIDS (14/20), epidemiology (9/20), anti-TB agents (6/20), and mortality (5/20). TB resistance was only considered a research priority in Brazil, Peru, and Haiti. Therefore, LA experienced significant growth in its scientific output, playing a crucial role in TB control. Strategic adaptation to the region's specific challenges was observed, particularly in HIV/AIDS coinfection, epidemiological studies, and drug resistance. This progress was achieved by outstanding international scientific collaboration. This holistic approach emphasizes the importance of research in the fight against TB in LA.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.100 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".