Control measures for neglected tropical diseases: vaccine updates
Bibliographic record
Abstract
INTRODUCTION: Infectious diseases like neglected tropical diseases (NTDs) have seen a rapid surge in recent times, threatening public health. These diseases impose a significant global health burden, affecting individuals, particularly in tropical locations characterized by low-income populations. The comprehensive compilation of NTDs includes an array of bacterial, viral, and parasitic infections. The prioritization of 20-NTD action plans in 2020 was undertaken by the WHO to acknowledge their importance. Infections such as leishmaniasis, schistosomiasis, and human African trypanosomiasis exhibit high rates of mortality. This highlights the pressing need for collaborative initiatives aimed at addressing these diseases and minimizing their detrimental impact on susceptible populations. AREAS COVERED: The etiology, types of NTDs, and management strategies, particularly vaccinations are discussed. The limitations of the available vaccines and the scope of development of novel formulations are also covered. EXPERT OPINION: The emergence of vaccines for NTDs poses significant challenges, mostly arising from the complex developmental phases of diverse diseases, inadequate resources for research, minimal involvement from the pharmaceutical industry, and the wide spectrum of infections, impeding vaccine development. Advancements in technology have improved vaccine quality, which could lead to the development of personalized vaccines tailored to individual susceptibility to specific NTD pathogens.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".