Evaluating the performance of common reference laboratory tests for acute dengue diagnosis: a systematic review and meta-analysis of RT-PCR, NS1 ELISA, and IgM ELISA
Bibliographic record
Abstract
BACKGROUND: Dengue fever is listed among the top ten global health threats by WHO. Prompt identification of dengue virus can guide clinical management and outbreak response, yet laboratory diagnosis is complex, costly, and lacks consensus on performance evaluation. This systematic review aims to provide reliable diagnostic accuracy estimates in order to inform global guidance and evaluate novel rapid diagnostic tests. METHODS: In this systematic review and meta-analysis, we searched nine literature databases on Feb 16, 2021, for reports on five common reference tests for dengue infection: NS1 ELISA, IgM ELISA, IgG ELISA, RT-PCR, and viral neutralisation test. Articles were included if they reported primary data from more than five participants to complete 2×2 tables comparing one of these tests (on human serum) with any comparator. Diagnostic accuracy was estimated using Bayesian random-effect meta-analysis, which does not require a gold-standard comparator. Risk of bias was assessed using QUADAS-2. This review is registered with PROSPERO (CRD42022341552). FINDINGS: Data were extracted from 161 articles, allowing analysis of multiple timeframes for three tests of interest. Pooled sensitivities of RT-PCR (0-4 days after symptom onset), NS1 ELISA (0-4 days), and IgM ELISA (1-7 days) were 95% (95% credible interval 77-99), 90% (68-98), and 71% (57-84), respectively. The corresponding pooled estimates of specificity were 89% (60-98), 93% (71-99), and 91% (82-95). A subanalysis of only studies at low risk of bias demonstrated similar estimates. INTERPRETATION: IgM ELISA shows poor diagnostic accuracy early in the symptom course. NS1 ELISA shows similar diagnostic accuracy to RT-PCR, which has important implications for global public health policy, given its relatively low cost and accessibility. FUNDING: None.
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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 | 0.029 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".