In-Hospital Delays for Acute Stroke Treatment Delivery During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: We investigated the impact of regionally imposed social and healthcare restrictions due to coronavirus disease 2019 (COVID-19) to the time metrics in the management of acute ischemic stroke patients admitted at the regional stroke referral site for Central South Ontario, Canada. METHODS: We compared relevant time metrics between patients with acute ischemic stroke receiving intravenous tissue plasminogen activator (tPA) and/or endovascular thrombectomy (EVT) before and after the declared restrictions and state of emergency imposed in our region (March 17, 2020). RESULTS: We identified a significant increase in the median door-to-CT times for patients receiving intravenous tPA (19 min, interquartile range (IQR): 14-27 min vs. 13 min, IQR: 9-17 min, p = 0.008) and/or EVT (20 min, IQR: 15-33 min vs. 11 min, IQR: 5-20 min, p = 0.035) after the start of social and healthcare restrictions in our region compared to the previous 12 months. For patients receiving intravenous tPA treatment, we also found a significant increase (p = 0.005) in the median door-to-needle time (61 min, IQR: 46-72 min vs. 37 min, IQR: 30-50 min). No delays in the time from symptom onset to hospital presentation were uncovered for patients receiving tPA and/or endovascular reperfusion treatments in the first 1.5 months after the establishment of regional and institutional restrictions due to the COVID-19 pandemic. CONCLUSION: We detected an increase in our institutional time to treatment metrics for acute ischemic stroke patients receiving tPA and/or endovascular reperfusion therapies, related to delays from hospital presentation to the acquisition of cranial CT imaging for both tPA- and EVT-treated patients, and an added delay to treatment with tPA.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".