Is monkeypox going to be the next pandemic?
Bibliographic record
Abstract
Background and aims: In the current global scenario, the monkeypox virus has infected over 3000 individuals from endemic countries like Nigeria, along with non-endemic countries like the UK, Canada, the USA, etc. Based on the current information, it has been observed that monkeypox cases have primarily, though not exclusively, been found among men who have sex with men (MSM) in countries such as the UK. This article discusses the recent outbreak of monkeypox, its causes, and the various approaches to combat monkeypox infections. Methods: We evaluated the trends of recent outbreaks of monkeypox in different countries and compared them to how the COVID-19 pandemic started. Results: At present, monkeypox has been reported to spread to over 58 countries via skin-to-skin contact, body fluids, contaminated bed sheets, clothing, or respiratory routes. Smallpox vaccines have been proven to have 85% efficacy against monkeypox. To mitigate this current outbreak, WHO urges people to practice good hygiene and safe sex. The documentation of more cases and further onward spread in the countries in member states are most likely to reoccur, and if not contained, we might experience another global pandemic. Therefore, more research is required to avert this problem. Conclusion: Monkeypox virus is testing if we have complied with COVID-19 pandemic lessons and elucidates the urgency of research required to understand the monkeypox disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".