Publication Trends in Neglected Tropical Diseases of Latin America and the Caribbean: A Bibliometric Analysis
Bibliographic record
Abstract
(1) Background: Neglected tropical diseases (NTDs) have been overlooked on the global health agenda and in the priorities of national systems in low- and middle-income countries (LMICs). In 2012, the Sustainable Development Goals (SDGs) were created to ensure healthy lives and promoting well-being for all. This roadmap set out to accelerate work to overcome the global impact of NTDs. Almost a decade has passed since NTDs were re-launched as a global priority. Investment in research and development, as well as the production of scientific literature on NTDs, is expected to have increased significantly. (2) Methods: A bibliometric analysis of the scientific production of Latin America and the Caribbean (LAC) was carried out in relation to 19 endemic NTDs. These data were compared with the scientific production in malaria, tuberculosis, and HIV/AIDS. The database available from Thomson Reuters Web of Science (WoS) was used. In addition, the average annual growth percentage was calculated for each disease. (3) Results: In the last decade, the NTDs with the highest number of publications in the world were dengue and leishmaniasis. The United States was the most prolific country in the world in 15 out of 19 NTDs analyzed. In the LAC region, Brazil was the largest contributor for 16 of the 19 NTDs analyzed. Arboviral diseases showed the highest average annual growth. The number of publications for malaria, tuberculosis and HIV/AIDS was considerably higher than for NTDs. The contribution of most LAC countries, especially those considered to be LMICs, is inadequate and does not reflect the relevance of NTDs for the public health of the population. (4) Conclusions: This is the first bibliometric analysis to assess the trend of scientific documents on endemic NTDs in LAC. Our results could be used by decision makers both to strengthen investment policies in research and development in NTDs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.178 | 0.243 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".