GloPID-R report on Chikungunya, O'nyong-nyong and Mayaro virus, part I: Biological diagnostics
Bibliographic record
Abstract
The GloPID-R (Global Research Collaboration for Infectious Disease Preparedness) Chikungunya (CHIKV), O'nyong-nyong (ONNV) and Mayaro virus (MAYV) Working Group is investigating the natural history, epidemiology and medical management of infection by these viruses, to identify knowledge gaps and to propose recommendations for direct future investigations and rectification measures. Here, we present the first report dedicated to diagnostic aspects of CHIKV, ONNV and MAYV. Regarding diagnosis of the disease at the acute phase, molecular assays previously described for the three viruses require further evaluation, standardized protocols and the availability of international standards representing the genetic diversity of the viruses. Detection of specific IgM would benefit from further investigations to clarify the extent of cross-reactivity among the three viruses, the sensitivity of the assays, and the possible interfering role of cryoglobulinaemia. Implementation of reference panels and external quality assessments for both molecular and serological assays is necessary. Regarding sero-epidemiological studies, there is no reported high-throughput assay that can distinguish among these different viruses in areas of potential co-circulation. New specific tools and/or improved standardized protocols are needed to enable large-scale epidemiological studies of public health relevance to be performed. Considering the high risk of future CHIKV, MAYV and ONNV outbreaks, the Working Group recommends that a major investigation should be initiated to fill the existing diagnostic gaps.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".