Molecular diagnostics for the detection of human flavivirus infections
Bibliographic record
Abstract
Flaviviruses constitute a genus of viruses that are important etiologic agents of human disease, causing clinical disease ranging from fever to severe manifestations, such as encephalitis and hemorrhagic fever. Serology is presently the most frequently used means of diagnosing flavivirus infections. However, other diagnostic tests may be employed, such as molecular detection, virus isolation and antigen-capture procedures. The applicability of the latter three diagnostic procedures can be expected to vary depending upon the infecting flavivirus, as some flaviviruses, such as dengue, display high and long-term viremias, whereas other flaviviruses produce no, or barely detectable, viremias. Molecular diagnostic techniques have been successfully applied to the diagnosis of flavivirus infections and have the advantage of rapidity, sensitivity and specific identification of the infecting virus. However, it is important to ensure that the right detection tools are employed (for example, appropriate primers and probes to detect the specific virus) and that the laboratory maintains a high proficiency in their testing procedures. Some of the studies that have been employed in the diagnosis of flavivirus infections are reviewed in this article. It seems that there is the potential to develop testing algorithms that successfully employ molecular diagnostics alone or in conjunction with other laboratory techniques for the diagnosis of acute human flavivirus infections.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".