A.1 Changes in ischemic stroke presentations and associated workflow during the first wave of the COVID-19 pandemic: A population study
Bibliographic record
Abstract
Background: Pandemics may promote hospital avoidance among patients with emergencies, and added precautions may exacerbate treatment delays. Methods: We used linked administrative data and data from the Quality Improvement and Clinical Research Alberta Stroke Program – a registry capturing stroke-related data on the entire Albertan population (4.3 million) – to identify all patients hospitalized with stroke in the pre-pandemic (01/01/2016-27/02/2020) and COVID-19 pandemic (28/02/2020-30/08/2020) periods. We examined changes in stroke presentation rates and use of thrombolysis and endovascular therapy (EVT), adjusted for age, sex, comorbidities, and pre-admission care needs; and in workflow, stroke severity (National Institutes of Health Stroke Scale/NIHSS), and in-hospital outcomes. Results: We analyzed 19,531 patients with ischemic stroke pre-pandemic versus 2,255 during the pandemic. Hospitalizations/presentations dropped (weekly adjusted-incidence-rate-ratio[aIRR]:0.48,95%CI:0.46-0.50), as did population-level incidence of thrombolysis (aIRR:0.49,0.44-0.56) or EVT (aIRR:0.59,0.49-0.69). However, proportions of presenting patients receiving thrombolysis/EVT did not decline (thrombolysis:11.7% pre-pandemic vs 13.1% during-pandemic, aOR:1.02,0.75-1.38). For out-of-hospital strokes, onset-to-door times were prolonged(adjusted-coefficient:37.0-minutes, 95%CI:16.5-57.5), and EVT recipients experienced greater door-to-reperfusion delays (adjusted-coefficient:18.7-minutes,1.45-36.0). NIHSS scores and in-hospital mortality did not differ. Conclusions: The first COVID-19 wave was associated with a halving of presentations and acute therapy utilization for ischemic stroke at a population level, and greater pre-/in-hospital treatment delays. Our data can inform public health messaging and stroke care in future pandemic waves.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".