The effects of COVID-19 on poisonings in the paediatric emergency department
Bibliographic record
Abstract
Objectives: The purpose of this study is to describe the impact of the pandemic on poisoning in children under 18 years presenting to a tertiary care paediatric emergency department (ED) in Canada. Methods: We utilized the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) surveillance data to identify children presenting to the Hospital for Sick Children for poisonings during two time periods: pre-pandemic (March 11 to December 31, 2018 and 2019) and pandemic (March 11 to December 31, 2020). Primary outcomes investigated the change in proportion for total poisonings, unintentional poisonings, recreational drug use, and intentional self-harm exposures over total ED visits. Secondarily, we examined the change in proportion of poisonings between age, sex, substance type, and admission requirement pre-pandemic versus during pandemic. Results: The proportions significantly increased for total poisonings (122.5%), unintentional poisonings (127.8%), recreational drug use (160%), and intentional self-harm poisonings (104.2%) over total ED visits. The proportions over all poisoning cases also significantly increased for cannabis (44.3%), vaping (134.6%), other recreational drugs (54.5%), multi-substance use (29.3%), and admissions due to poisonings (44.3%) during the pandemic. Conclusion: Despite an overall decrease in ED visits, there was a significant increase in poisoning presentations to our ED during the pandemic compared with pre-pandemic years. Our results will provide better insight into care delivery and public health interventions for paediatric poisonings.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".