Reemergence of monkeypox: prevention and management
Bibliographic record
Abstract
INTRODUCTION: The reemergence of monkeypox virus in the twenty-first century, calls for an urgency in its control and preventive measures. There is a long-standing concern that the reemergence of monkeypox across countries could lead to another epidemic like the COVID-19 pandemic. Understanding the disease ecology, preventing its transmission could help curbing its spread. The established treatment protocols along with development of new antiviral agents and vaccines could play a pivotal role in controlling its transmission. AREAS COVERED: In this review, we summarize the different modes of transmission of this disease, the associated symptoms, the standard protocol of treatment, the available vaccines and use of alternative treatments. We have collated recent research on novel entities that could potentially treat monkeypox infection. EXPERT OPINION: The One Health approach fostered by the World Health Organization (WHO) for emergent and reemerging zoonotic diseases has to be implemented with a view to curb their transmission. The growing global population and increased inter-country travel has led to rapid spread of transmissible pathogens. Stigmatization, associated with lack of knowledge can be prevented by enhancing awareness campaigns. Vaccines need to be administered to high-risk individuals, and drug discovery efforts need to be intensified to combat such diseases.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".