A dengue vaccine whirlwind update
Bibliographic record
Abstract
Dengue virus (DENV) is a mosquito-borne single-stranded RNA virus of the Flaviviridae family with four serotypes (DENV1, DENV2, DENV3, and DENV4) circulating many tropical and subtropical regions of the world. Endemic in more than 100 countries, DENV results in over 400 million cases annually, a subset presenting with severe or life-threatening illnesses such as dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). While no specific treatments outside of supportive management exist, vaccines are an area of major research with two vaccines, Dengvaxia ® (CYD-TDV) and Denvax ® (TAK003), recently licensed for clinical use. CYD-TDV is highly efficacious in children 9 years or older who have had prior DENV infection due to the high risk of severe disease in seronegative children aged 2–5 years. Meanwhile, TAK003 has shown efficacy at 97.7% and 73.7% against, DENV2 and DENV1, respectively, in phase 3 clinical trials across Latin America and Asia in healthy children aged 4–16 with virologically confirmed dengue. Other vaccines including TV003 and TV005 continue to be developed across the world, with the hopes of entering clinical trials in the near future. We discuss the current state of vaccine development against dengue, with a focus on CYD-TDV and TAK003 as promising novel vaccines to target this neglected tropical disease (NTD).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.043 | 0.020 |
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".