Vaccine value profile for leishmaniasis
Bibliographic record
Abstract
Leishmania infections are global, occurring in 98 countries and all World Health Organization (WHO) regions with 600 million to 1 billion people at risk of infection. Visceral leishmaniasis is associated with almost 20,000 reported deaths annually, with children under 5 years of age being at the greatest risk of mortality. Amongst WHO-recognised Neglected Tropical Diseases (NTDs), leishmaniasis is one of the most important in terms of mortality and morbidity. With an increasing global burden of disease and a growing threat from climate change, urbanisation and drug resistance, there remains an imperative to develop leishmaniasis vaccines. New tools to understand correlates of protection and to assess vaccine efficacy are being developed to ease the transition into larger scale efficacy trials or provide alternate routes to licensure. Early indications suggest a diverse portfolio of manufacturers exists in endemic countries with an appetite to develop leishmaniasis vaccines. This Vaccine Value Profile (VVP) provides a high-level, comprehensive assessment of the currently available data to inform the potential public health, economic, and societal value of leishmaniasis vaccines. The leishmaniasis VVP was developed by a working group of subject matter experts from academia, public health groups, policy organizations, and non-profit organizations. All contributors have extensive expertise on various elements of the leishmaniasis VVP and have collectively described the state of knowledge and identified the current gaps. The VVP was developed using only existing and publicly available information.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".