Comparison of the Loop-Mediated Isothermal Amplification (LAMP) and the Kato-Katz Techniques in the Diagnosis of <i>Schistosoma mansoni</i> in Burkina Faso
Bibliographic record
Abstract
Intestinal schistosomiasis or intestinal bilharzia, mainly caused by Schistosoma mansoni, is one of the most common parasitic diseases in the world, and a neglected tropical disease (NTD). It ranks first among water-borne diseases and is the 2nd most endemic parasitic disease after malaria and according to the World Health Organization (WHO), schistosomiasis is transmitted in more than 78 countries and territories in tropical and subtropical regions, and more than 250 million people are infected, mainly in Africa. Kato Katz (KK) remains the standard technique for diagnosing this disease. A promising new approach, loop-mediated isothermal amplification (LAMP), may be needed in developing countries such as Burkina Faso. Thus, the aim of this study was to compare the LAMP technique and the Kato-Katz technique in the diagnosis of Schistosoma mansoni in Burkina Faso. 52 stool samples were collected from patients in the town of Bobo Dioulasso and examined using the KK technique, which corresponds to microscopy and the LAMP technique, to assess the sensitivity and specificity of this molecular technique. The results showed a prevalence of intestinal schistosomiasis of 8% in the study, and the Kappa coefficient obtained between the 2 techniques was 0.99, roughly equal to 1. The sensitivity and specificity of the LAMP molecular test was 100%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".