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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".