Evaluating a rapid molecular assay in a mobile laboratory for improved diagnosis of dengue in Bangladesh
Bibliographic record
Abstract
OBJECTIVES: Dengue emerged as a significant health threat in endemic regions in recent years. However, inconsistent diagnostic accuracy in sequential dengue infections necessitate improved testing methods to ensure effective management of dengue cases. Here, we evaluated a portable, rapid, and sensitive molecular assay-reverse transcriptase recombinase polymerase amplification assay (RT-RAA)-utilizing a mobile suitcase laboratory to detect infections in suspected dengue cases in Bangladesh. METHODS: A total of 364 suspected patients with dengue were enrolled in the study. Dengue cases were confirmed by a positive result from any of the four tests: non-structural protein 1 (NS1) rapid diagnostic test (RDT), immunoglobulin (Ig) M RDT, quantitative reverse transcriptive-polymerase chain reaction (RT-PCR), and RT-RAA assay. IgG RDT was performed to differentiate between primary and secondary dengue infections. RESULTS: Of 364 suspected cases, 320 were confirmed dengue cases, with 55.94% classified as primary and 44.06% as secondary infections. Laboratory results showed comparable positivity rates between RT-RAA (78.8%) and NS1 RDT (77.1%) in primary dengue, followed by quantitative RT-PCR (57.5%) and IgM RDT (12.8%). RT-RAA demonstrated superior positivity rates in secondary dengue (76.6%), surpassing RT-PCR (60.3%), NS1 RDT (27%), and IgM RDT (24.8%). Combining RT-RAA with NS1 RDT detected infections in 89.95% primary and 81.56% secondary dengue. CONCLUSIONS: The findings suggest that complementing RT-RAA with NS1 RDT could significantly improve dengue detection rate, particularly, for secondary infections.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".