Performance of loop-mediated isothermal amplification (LAMP) for the diagnosis of malaria among malaria suspected pregnant women in Northwest Ethiopia
Bibliographic record
Abstract
BACKGROUND: Malaria is a major public health problem and an important cause of maternal and infant morbidity in sub-Saharan Africa, including Ethiopia. Early and accurate diagnosis of malaria with effective treatment is the best strategy for prevention and control of complications during pregnancy and infant morbidity and mortality. However, laboratory diagnosis has relied on the identification of malaria parasites and parasite antigens in peripheral blood using Giemsa-stained microscopy or rapid diagnostic tests (RDTs) which lack analytical and clinical sensitivity. The aim of this study was to evaluate the performance of loop-mediated isothermal amplification (LAMP) for the diagnosis of malaria among malaria suspected pregnant women in Northwest Ethiopia. METHODS: A cross sectional study was conducted from January to April 2016. Pregnant women (n = 87) suspected of having malaria at six health centres were enrolled. A venous blood sample was collected from each study subject, and analysed for Plasmodium parasites by microscopy, RDT, and LAMP. Diagnostic accuracy outcome measures (sensitivity, specificity, predictive values, and Kappa scores) of microscopy, RDT and LAMP were compared to nested polymerase chain reaction (nPCR) as the gold standard. Specimen processing and reporting times were documented. RESULTS: Using nPCR as the gold standard technique, the sensitivity of microscopy and RDT was 90 and 70%, and the specificity was 98.7 and 97.4%, respectively. LAMP assay was 100% sensitive and 93.5% specific compared to nPCR. CONCLUSIONS: This study showed higher sensitivity of LAMP compared to microscopy and RDT for the detection of malaria in pregnancy. Increased sensitivity and ease of use with LAMP in point-of-care testing for malaria in pregnancy was noted. LAMP warrants further evaluation in intermittent screening and treatment programmes in pregnancy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".