Misinformation During the Coronavirus Disease 2019 Outbreak: How Knowledge Emerges From Noise
Bibliographic record
Abstract
Although the amount of information generated during this most recent coronavirus disease 2019 pandemic is enormous, much is of uncertain trustworthiness. This review summaries the many potential sources of information that clinicians turn to during pandemic illness, the challenges associated with performing methodologically sound research in this setting and potential approaching to conducting well done research during a health crisis. DATA SOURCES: Not applicable. STUDY SELECTION: Not applicable. DATA EXTRACTION: Not applicable. DATA SYNTHESIS: Not applicable. CONCLUSIONS: Pandemics and healthcare crises provide extraordinary opportunities for the rapid generation of reliable scientific information but also for misinformation, especially in the early phases, which may contribute to public hysteria. The best way to combat misinformation is with trustworthy data produced by healthcare researchers. Although challenging, research can occur during pandemics and crises and is facilitated by advance planning, governmental support, targeted funding opportunities, and collaboration with industry partners. The coronavirus disease 2019 research response has highlighted both the dangers of misinformation as well as the benefits and possibilities of performing rigorous research during challenging times.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".