The Impact of COVID-19 on the Service of Emergency Department
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
(1) Introduction: the COVID-19 pandemic significantly impacted the number and acuity of emergency departments (ED) patients, specifically those with non-COVID-19-related health problems. However, the exact impact of the COVID-19 pandemic on ED services is the subject of comprehensive debate. (2) Aim: to gain insight into the consequences of the first wave of the COVID-19 pandemic based on non-COVID-19 presentations and patient acuity using the Canadian Triage and Acuity Scale (CTAS). (3) Method: in Phase 1, the ED records of one of the main regional non-COVID-19 hospitals in Saudi Arabia were retrospectively audited from August 2020 to February 2021—after the first wave of COVID-19—then compared to information collected for the same period in previous year. Phase 2 included calculating the waiting time to identify delays and issues that may impact the triage effectiveness. (4) Results: a change across all CTAS levels was observed post the 1st wave of COVID-19 pandemic. Specifically, there was an increase in the number of patients presenting as higher acuity (CTAS 1 and 2) and a decrease in patients presenting as lower acuity (CTAS 4 and 5). Longer waiting times for patients presenting to ED were also reported. Specifically, 83% of patients presenting as higher acuity experienced a delay. (5) Conclusion: further studies are required to investigate association between the 1st wave of COVID-19 and patient presentations and/or acuity or patient demand and ED capacity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it