Clinical Validation of a Commercial LAMP Test for Ruling out Malaria in Returning Travelers: A Prospective Diagnostic Trial
Bibliographic record
Abstract
Malaria assay (loop-mediated isothermal amplification [LAMP]) was conducted comparing it with reference microscopy and RDTs (BinaxNOW Malaria) in returning travelers between June 2017 and January 2018. Returning travelers with signs and symptoms of malaria were enrolled in the study. RDTs, microscopy, and LAMP assays were performed simultaneously. A total of 298 patients (50.7% male; mean age, 32.5 years) were enrolled, most visiting friends and relatives (43.3%), presenting with fever (88.9%), not taking prophylaxis (82.9%), and treated as outpatients (84.1%). In the prospective arm (n = 348), LAMP had a sensitivity of 98.1% (95% confidence interval [CI], 90.0%-100%) and a specificity of 97.6% (95% CI, 95.2%-99.1%) vs microscopy. After discrepant resolution with real-time polymerase chain reaction, LAMP had a sensitivity of 100% (95% CI, 93.7%-100%) and a specificity of 100% (95% CI, 98.7%-100%) vs microscopy. After discrepant resolution, RDTs had a sensitivity of 83.3% (95% CI, 58.6%-96.4%) and a specificity of 96.2% (95% CI, 93.2%-98.1%) vs microscopy. When including retrospective specimens (n = 377), LAMP had a sensitivity of 98.8% (95% CI, 93.2%-100%) and a specificity of 97.6% (95% CI, 95.2%-99.1%) vs microscopy, and after discrepant resolution of this set, LAMP had a sensitivity of 100% (95% CI, 95.8%-100%) and a specificity of 100% (95% CI, 98.7%-100%). A cost-benefit analysis of reagents and labor suggests savings of up to USD$13 per specimen using a novel algorithm with LAMP screening.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".