Atovaquone–Proguanil for Prophylaxis and Treatment of Malaria
Bibliographic record
Abstract
OBJECTIVE: To review the currently available information on atovaquone-proguanil for treatment and prophylaxis of malaria. DATA SOURCES: A MEDLINE search was conducted from 1966 to February 2003 using key phrases Malarone, atovaquone, proguanil, and malaria. Further articles were identified from a manual search of the references of identified articles. STUDY SELECTION AND DATA EXTRACTION: English-language studies with animal and human data evaluating preclinical pharmacology, human studies on pharmacokinetics, and clinical trials were evaluated. Relevant data were extracted from identified articles. DATA SYNTHESIS: Atovaquone-proguanil has been evaluated for treatment of acute, uncomplicated malaria caused by Plasmodium falciparum in 8 clinical trials. In these studies, treatment with atovaquone-proguanil led to a higher (87-100% vs. 72-88%) or equally effective (94-100% vs. 90-100%) cure rate than the comparator antimalarial agents. Atovaquone-proguanil has been evaluated for prophylaxis of malaria in 6 clinical trials. In the 4 placebo-controlled trials for semi-immune residents or nonimmune migrants, the prophylaxis success rates in the atovaquone-proguanil and placebo arms ranged from 98% to 100% and 48% to 82%, respectively. The prophylaxis with success rates were similar among the 2 arms when atovaquone-proguanil was compared with other antimalarial regimens in nonimmune travelers. Atovaquone-proguanil was well tolerated in these clinical trials. CONCLUSIONS: Atovaquone-proguanil is a safe and effective alternative to current recommended regimens for prophylaxis and treatment of malaria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".