COVID-19: Stroke Admissions, Emergency Department Visits, and Prevention Clinic Referrals
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
We assessed the impact of the coronavirus disease 19 (COVID-19) pandemic on code stroke activations in the emergency department, stroke unit admissions, and referrals to the stroke prevention clinic at London's regional stroke center, serving a population of 1.8 million in Ontario, Canada. We found a 20% drop in the number of code strokes in 2020 compared to 2019, immediately after the first cases of COVID-19 were officially confirmed. There were no changes in the number of stroke admissions and there was a 22% decrease in the number of clinic referrals, only after the provincial lockdown. Our findings suggest that the decrease in code strokes was mainly driven by patient-related factors such as fear to be exposed to the SARS-CoV-2, while the reduction in clinic referrals was largely explained by hospital policies and the Government lockdown.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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