Malaria Rapid Diagnostic Devices: Performance Characteristics of the <i>Para</i> Sight F Device Determined in a Multisite Field Study
Bibliographic record
Abstract
Microscopic detection of parasites has been the reference standard for malaria diagnosis for decades. However, difficulty in maintaining required technical skills and infrastructure has spurred the development of several nonmicroscopic malaria rapid diagnostic devices based on the detection of malaria parasite antigen in whole blood. The ParaSight F test is one such device. It detects the presence of Plasmodium falciparum-specific histidine-rich protein 2 by using an antigen-capture immunochromatographic strip format. The present study was conducted at outpatient malaria clinics in Iquitos, Peru, and Maesod, Thailand. Duplicate, blinded, expert microscopy was employed as the reference standard for evaluating device performance. Of 2,988 eligible patients, microscopy showed that 547 (18%) had P. falciparum, 658 (22%) had P. vivax, 2 (0.07%) had P. malariae, and 1,750 (59%) were negative for Plasmodium. Mixed infections (P. falciparum and P. vivax) were identified in 31 patients (1%). The overall sensitivity of ParaSight F for P. falciparum was 95%. When stratified by magnitude of parasitemia (no. of asexual parasites per microliter of whole blood), sensitivities were 83% (>0 to 500 parasites/microl), 87% (501 to 1,000/microl), 98% (1,001 to 5,000/microl), and 98% (>5,000/microl). Device specificity was 86%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".