Laboratory evaluation of antigen rapid diagnostic tests to detect Ebola and Sudan viruses
Bibliographic record
Abstract
BACKGROUND: Nucleic acid-based assays are the diagnostic gold standard for filoviruses, including Ebola (EBOV) and Sudan (SUDV) viruses. However, outbreaks in areas with limited laboratory infrastructure highlight the need for simpler diagnostic tests that can be rapidly and safely used in the field. METHODS: We evaluated eight antigen rapid diagnostic tests (Ag-RDTs) for their ability to detect EBOV and SUDV. Analytical panels using virus cell slurries were used to assess limit of detection, and clinical samples were tested to determine sensitivity and specificity. RESULTS: Five Ag-RDTs detected EBOV and three detected SUDV, although clinical sensitivity was low (20-40 % for EBOV, 33 % for SUDV), improving only with higher viral loads. All assays demonstrated 100 % clinical specificity with no cross-reactivity. DISCUSSION: Although none of the evaluated Ag-RDTs are suitable for routine diagnosis, some may be useful in high viral load contexts such as cadaver testing. Our findings highlight the need to improve Ag-RDT sensitivity or develop high-sensitivity point-of-care molecular diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".