112 Impact of the COVID-19 pandemic on emergency department visits at a university medical centre
Bibliographic record
Abstract
Abstract Primary Subject area Emergency Medicine - Paediatric Background The new coronavirus, SARS-Cov-2, responsible for a global pandemic, led to the declaration of a health emergency and the implementation of large-scale public health measures by governments in 2020. Those measures, combined with the overlapping symptoms of COVID-19 disease and common viral infections in children, have led hospitals to prepare for possible changes in volume of emergency room visits by children. Objectives This study aims to determine the effect of the pandemic and governmental restrictions on the use of the emergency department by pediatric patients at a university medical centre, as well as to assess the impact on the severity of initial presentations. Design/Methods A single-centre study was conducted at a university hospital among children aged 0 to 17 who visited the emergency room. We used interrupted time series analysis to compare the average of pre-COVID-19 data (from January 1 2017, to December 31 2019) with data from the first wave of the COVID-19 pandemic (from January 1 to September 2020). Emergency room visits, initial triage codes, and admission and pediatric consultation rates were analyzed to assess whether there was a significant difference between periods. Results An important increase in total daily visits (+11.18, 95% CI [6.23-16.14]) was first observed with the emergence of COVID-19 cases in Canada. Then, during the strict confinement, which corresponds to an active period of COVID-19, we detected a significant drop in daily visits (-25.64, 95% CI [-30.4 to -20.66]), which continued while progressively loosening restrictions. The proportion of admissions and pediatric consultations rose slightly only throughout the time of intensive health measures (respectively +4.07% and +3.32%), but no changes in the severity of the triage codes at the emergency department were observed for all periods. Traumatic injuries also saw a significant decrease (p=0.018) when comparing data by groups of diagnosis. Conclusion These results show a significantly lower number of children’s visits to the emergency room of a university medical centre, and a transient increase in pediatric care with little impact on the immediate severity of the initial presentations during strict government health measures in the first wave of COVID-19. These measures also had a beneficial effect in reducing the number of traumatic injuries.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".