Malaria Screening Using Front-Line Loop-Mediated Isothermal Amplification
Bibliographic record
Abstract
OBJECTIVES: We implemented front-line loop-mediated isothermal amplification (LAMP)-based malaria screening in our nonendemic multicenter health region to reduce reliance on microscopy without sacrificing diagnostic efficiency. We aimed to evaluate changes in test volumes, positivity rates, turnaround times, and approximate labor time savings resulting from implementation of LAMP-based malaria testing to assess the efficacy of the novel testing algorithm in our regional hub-and-spoke testing model. METHODS: We reviewed data generated from institutional malaria testing between 2016 and 2019, having implemented LAMP in October 2018 as a front-line screening test for all malaria investigations from our hub facility and investigations from satellite facilities with negative rapid diagnostic tests (RDTs) and microscopy. RESULTS: Blood film microscopy and RDT workloads decreased substantially in the year following LAMP implementation (by 90% and 46%, respectively,) despite similar numbers of patients tested and positivity rates for malaria compared with historical data. LAMP turnaround times (TATs) were comparable to historical TATs for RDTs, and TATs for RDTs and thick films did not increase with the change in workflow. CONCLUSIONS: LAMP was successfully implemented in our multicenter health region malaria diagnostic algorithm, significantly reducing reliance on microscopic evaluations and RDT and providing substantial labor time savings without compromising TATs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".