Adjunctive therapy for severe malaria: a review and critical appraisal
Bibliographic record
Abstract
BACKGROUND: Despite recent efforts and successes in reducing the malaria burden globally, this infection still accounts for an estimated 212 million clinical cases, 2 million severe malaria cases, and approximately 429,000 deaths annually. Even with the routine use of effective anti-malarial drugs, the case fatality rate for severe malaria remains unacceptably high, with cerebral malaria being one of the most life-threatening complications. Up to one-third of cerebral malaria survivors are left with long-term cognitive and neurological deficits. From a population point of view, the decrease of malaria transmission may jeopardize the development of naturally acquired immunity against the infection, leading to fewer total cases, but potentially an increase in severe cases. The pathophysiology of severe and cerebral malaria is not completely understood, but both parasite and host determinants contribute to its onset and outcomes. Adjunctive therapy, based on modulating the host response to infection, could help to improve the outcomes achieved with specific anti-malarial therapy. RESULTS AND CONCLUSIONS: In the last decades, several interventions targeting different pathways have been tested. However, none of these strategies have demonstrated clear beneficial effects, and some have shown deleterious outcomes. This review aims to summarize evidence from clinical trials testing different adjunctive therapy for severe and cerebral malaria in humans. It also highlights some preclinical studies which have evaluated novel strategies and other candidate therapeutics that may be evaluated in future clinical trials.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".