Early Diagnosis of Dengue Infection Using a Commercial Dengue Duo Rapid Test Kit for the Detection of NS1, IGM, and IGG
Bibliographic record
Abstract
A commercial Dengue Duo rapid test kit was evaluated for early dengue diagnosis by detection of dengue virus NS1 antigen and immunoglobulin M (IgM)/IgG antibodies. A total of 420 patient serum samples were subjected to real-time reverse transcription-polymerase chain reaction (RT-PCR), in-house IgM capture enzyme-linked immunosorbent assay (ELISA), hemagglutination inhibition assay, and the SD Dengue Duo rapid test. Of the 320 dengue acute and convalescent sera, dengue infection was detected by either serology or RT-PCR in 300 samples (93.75%), as compared with 289 samples (90.31%) in the combined SD Duo NS1/IgM. The NS1 detection rate is inversely proportional, whereas the IgM detection rate is directly proportional to the presence of IgG antibodies. The sensitivity and specificity in diagnosing acute dengue infection in the SD Duo NS1/IgM were 88.65% and 98.75%, respectively. The assay is sensitive and highly specific. Detection of both NS1 and IgM by SD Duo gave comparable detection rate by either serology or RT-PCR.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".