Abstract P141: Global Impact of Covid-19 Pandemic on Acute Stroke and Mechanical Thrombectomy - An International Survey
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has strained the healthcare systems across the world but its impact on acute stroke care is just being elucidated. We hypothesized a major global impact of COVID-19 not only on stroke volumes but also on thrombectomy practice. Methods: A 19-item questionnaire survey aimed to identify the changes in stroke volumes and treatment practices seen during COVID-19 pandemic was designed using Qualtrics software. It was sent to stroke and neuro-interventional physicians around the world who are part of the executive committee of a global coalition, Mission Thrombectomy 2020 (MT2020) between April 5 th to May 15 th , 2020. Results: There were 113 responses across 25 countries. Globally there was a median 33% decrease in stroke admissions and a 25% decrease in mechanical thrombectomy (MT) procedures during COVID-19 pandemic compared to immediately preceding months (Figure 1A-B). This overall median decrease was despite a median increase in stroke volume in 4 European countries which diverted all stroke patients to only a few selected centers during the pandemic. The intubation policy during the pandemic for patients undergoing MT was highly variable across participating centers: 44% preferred intubating all patients, including 25% centers that changed their policy to preferred-intubation (PI) vs 27% centers that switched to preferred-conscious-sedation (PCS). There was no significant difference in rate of COVID-19 infection between PI vs PCS (p=0.6) or if intubation policy was changed in either direction (p=1). Low-volume (<10 stroke/month) compared with high-volume stroke centers (>20 strokes/month) are less likely to have neurointerventional suite specific written personal protective equipment protocols (74% vs 88%) and if present, these centers are more likely to report them to be inadequate (58% vs 92%). Conclusion: Our data provides a comprehensive snapshot of the impact on acute stroke care observed worldwide during the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".