Maintaining high thrombectomy rates during pandemics
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to review the current literature on endovascular treatment of acute ischemic stroke in the aftermath of the coronavirus disease 2019 (COVID-19) lockdown. RECENT FINDINGS: The outbreak of the COVID-19 has had effect of unprecedented magnitude on the social, economic and personal aspects around the globe. Healthcare providers were forced to expand capacity to provide care to the surging number of symptomatic COVID-19 patients, while maintaining a fully operating service for all non-COVID patients. The recent literature suggesting an overall decrease in acute ischemic stroke admissions as well as total number of endovascular treatments will be reviewed. Although the underlying reasons therefore remain the matter of debate, it seems that the imposed restrictions, requiring social distancing, and stopping all nonessential services, have led to a higher threshold for patients to seek medical attention, in particular in those with less severe symptoms. Thus, raising public awareness on the importance of strokes and transient ischemic attacks is even more important in the light of the current situation to avoid serious healthcare, economic consequences, and limit long term morbidity. SUMMARY: The priority remains maintaining a fast and efficient pre and in-hospital work-flow while mitigating nosocomial transmission and protecting the patient and the healthcare workers with appropriate personal protective equipment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".