Emergency Medical Service Facilitated Geriatric Emergency Department Visits in Hamilton, Ontario, Canada During the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: To determine if lockdown measures related to the COVID-19 pandemic changed the frequency and epidemiology of geriatric patient emergency medical service (EMS) facilitated visits to the emergency department (ED) in Hamilton, Ontario, Canada. Method: A retrospective chart review was conducted comparing ED presentations of patients over 65 years of age presenting to two academic hospitals in Hamilton, Ontario via EMS between March 17, 2020, and July 15, 2020 (the first wave of the COVID-19 pandemic) to March 17, 2019, and July 15, 2019 (pre-pandemic). Results: Total EMS facilitated geriatric ED number of visits decreased by 17.3% during the first wave of COVID-19 in 2020, relative to the same seasonal time frame in 2019 (March 17- July 15). Visits were more dramatically decreased in the first 8 weeks after the pandemic was declared but then recovered to pre-pandemic levels thereafter. More geriatric patients visiting the ED via EMS were admitted during the initial stages of the COVID-19 pandemic, relative to 2019. However, the acuity and epidemiology of visits remained the same during the first wave of the COVID-19 pandemic, relative to 2019. Conclusion: Lockdown measures during the first wave of the COVID-19 pandemic coincided with decreased geriatric EMS ED visits in the initial two months after the pandemic was declared. Visit numbers recovered as the first wave ended. The epidemiology, as well as the overall acuity, did not change.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".