Event-Based Surveillance of Poisonings and Potentially Hazardous Exposures over 12 Months of the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has seen people and governments utilise an array of chemical and pharmaceutical substances in an attempt to prevent and treat COVID-19 infections. The Centre for Radiation, Chemicals and Environmental Hazards (CRCE) at Public Health England (PHE) routinely undertakes Event-Based Surveillance (EBS) to monitor public health threats and incidents related to chemicals and poisons. From April 2020, EBS functions were expanded to screen international media for potentially hazardous exposures associated with the COVID-19 pandemic. Media sources reported that poisons centres were experiencing increased enquiries associated with the use and misuse of household cleaners and alcohol-based hand sanitiser (HS). There were also media reports of people self-medicating with over-the-counter supplements and traditional or herbal remedies. Public figures who directly or indirectly facilitated misinformation were sometimes reported to be associated with changes in poisoning trends. Border closures were also believed to have been associated with increasingly toxic illicit drug supplies in Canada, and record numbers of opioid-related deaths were reported. In other countries, where the sale of alcohol was banned or limited, home-brewing and methanol-based supplies resulted in a number of fatalities. At least two chemical incidents also occurred at industrial sites in India, after sites were left unattended or were closed and reopened due to lockdown measures. Reports of poisoning identified in the international media were provided to the UK National Poisons Information Service (NPIS) and contributed to the UK COVID-19 public health response.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".