Methods for conducting a living evidence profile on mpox: An evidence map of the literature
Bibliographic record
Abstract
Background: In May of 2022, several cases of mpox were identified in multiple nonendemic countries and on July 23, 2022 the World Health Organization declared mpox a Public Health Emergency of International Concern. During the first six months of the outbreak there was an urgent need to have up-to-date synthesized evidence on mpox to inform public health decision-making. At this point, evidence is changing too quickly for traditional evidence synthesis methods, as systematic reviews were out-of-date before publication. This paper describes the framework developed to manage and maintain a living evidence profile (LEP) to systematically identify, classify and synthesize evidence on a broad range of mpox topics at a rapid pace as the outbreak unfolded. Methods: The LEP framework was based on principles of evidence synthesis, risk assessment, priority epidemiological parameters for infectious disease modeling and consultation with experts. The framework consisted of a systematic search conducted twice weekly; study selection; categorization into pre-determined foci and data extraction; integration and synthesis of evidence; internal peer-review and dissemination to stakeholders. Results: Between April 14 and December 15, 2022, 2287 citations were identified, 687 were primary research studies or surveillance reports on the 2022 mpox outbreak and 496 were included in the final LEP. Each study was mapped to one of 32 foci and evidence was narratively synthesized. From June to December 2022, 23 LEPs were produced (approximately weekly) along with a searchable database of extracted data of the mpox literature. They were disseminated globally to public health researchers and decision-makers to inform public health response efforts. Conclusions: The LEP framework is applicable to other public health emergencies when a rapid synthesis cycle is required because the evidence is evolving quickly. This efficient methodology for creating up-to-date summaries of the current evidence during the first few months of an outbreak or emergency supports public health decision-making and response activities.
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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 | 0.261 | 0.384 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.100 | 0.061 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".