Clinical and laboratory diagnosis of Mayaro virus (MAYV): Current status and opportunities for further development
Bibliographic record
Abstract
The recent outbreaks related to Mayaro virus (MAYV) infection in the Americas have brought this neglected virus as a potential threat to global public health. Given the range of symptoms that can be associated with MAYV infection, it can be challenging to diagnose individuals based on clinical signs, especially in countries with simultaneous circulation of other mosquito-borne viruses, such as dengue virus (DENV) and chikungunya virus (CHIKV). With this challenge in mind, laboratory-based diagnosis assumes a critical role in the introduction of measures to help prevent virus dissemination and to adequately treat patients. In this review, we provide an overview of the clinical features reported in infected patients and currently available laboratory tools that are used for MAYV diagnosis, discussing their advances, advantages, and limitations to apply in the field. Moreover, we explore novel point-of-care (PoC) diagnostic platforms that can provide de-centralised diagnostics for use in areas with limited laboratory infrastructure.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".