Field Evaluation of Mobile Molecular Differential Tests in DRC and Nigeria
Bibliographic record
Abstract
Abstract Background Accurate and timely differential diagnoses are a challenge for health care, particularly in infrastructure-poor settings. Methods To investigate fevers of unknown origin in Africa, a mobile suitcase laboratory was deployed to DRC and Southwest Nigeria to support the control of the 2018–2020 Ebola virus disease outbreak in North-Kivu and Ituri provinces (DRC) and to provide a point-of-need solution for malaria confirmation during the dry season, respectively. Results In DRC, the samples were tested for Ebola virus and the differentials Plasmodium falciparum, Salmonella enterica, yellow fever virus, Dengue virus, and chikungunya virus. In Southwest Nigeria, the samples were not tested for Ebola virus but were tested for the same differentials and additionally for Rickettsia spp., Leptospira, and Streptococcus pneumoniae. Plasmodium falciparum was detected in 23% (n = 192) and 47% (n = 88) of cases, respectively, and Salmonella enterica was detected in only 1 case in each cohort. Conclusions The etiological agents circulating in febrile patients in Sub-Saharan Africa and the true incidence of neglected tropical diseases are still underestimated.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".