Time Is Brain: A Call to Action to Support Stroke Centers in Low- and Middle-Income Countries during the COVID-19 Pandemic
Bibliographic record
Abstract
Dear Editor,COVID-19 infections may affect other noncommunicable diseases, such as stroke, both directly and indirectly. Stroke may increase the probability of severe infection and mortality in COVID-19 infections. COVID-19 may lead to cardiac injury, arrhythmia, myocarditis, coagulopathy, and consequently stroke [1]. As an example, in Iran, neurologists identified some stroke cases among COVID-19 cases.Given the health infrastructure differences, economic differences, and previous differences in the burden of stroke between low- and middle-income (LMICs) versus high-income countries [2, 3], LMICs, in particular, may face a considerable strain with a possible negative impact on the healthcare delivery system. While we are globally fighting COVID-19, we need to implement feasible approaches to prevent or at least minimize any breakdown in the previous preventive and treatment approaches. We here recommend some suggestions and a call to action in LMICs.Healthcare professionals are at a higher risk of COVID-19 than the normal population [4]. Given the previous staff shortages in healthcare in many LMICs, providing personal protection equipment should be prioritized. We encourage international organizations, such as the World Federation of Neurology (WFN) and World Stroke Organization (WSO), to help provide personal protection equipment for LMICs.Social isolation does not mean lack of social interactions. We recommend health policymakers to authorize telemedicine technology in LMICs. Many important aspects of acute stroke management, from EMS dispatch to the selection of eligible cases for intravenous thrombolysis or endovascular therapy, can be performed via Telestroke [5].Expensive telemedicine and telerehab programs are not appropriate for many LMIC situations. Low-priced, accessible, and secured cross-platform mobile applications can facilitate telemedicine/telerehab usage. Institutions may explore with the Ethics Committee and Institutional Review Board whether commercially available low-cost smartphone applications can substitute in locations where Telestroke networks are not available. Besides, the security and confidentiality of telemedicine systems should be assured and perhaps funded by social media companies.Early supported discharge services should be organized in LMICs [6]. Stroke centers need to practice 24/7 outpatient support using secured video-audio applications or at least telephone questionnaires to address vascular risk factors and assure medication compliance. Important screening questions with simple preventive measures to reduce the chance of deep vein thrombosis, falls, urinary tract infections, and pneumonia could be sent using Bluetooth/email to patients or the next of kin instead of paper-based documents. Based on anecdotal data, some crucial investigations such as formal swallowing assessments were reduced in some centers for fear of contagion.All unnecessary diagnostic activities should be summarized or merged for the sake of decreasing the rate of, or at least the fear of, COVID-19. If possible, it is crucial to separate CT units of COVID-19 cases from all other cases. Neurologists can send Holter monitoring to selected patients along with installation instructions or a telephone call for installation and a prepaid envelope to mail it back.Finally, stroke centers in the world need to support each other. Worldwide prestigious neurology centers, for example, WSO or WFN, can host a network of centers to share the best policies and experiences. LMICs need to be supported to continue fighting against stroke. Neurology is a big but close-knit family.The authors have no conflicts of interest to declare.The authors did not receive any funding.Study concepts, study design, manuscript preparation, definition of intellectual content, manuscript editing, and manuscript revision/review: Afshin Borhani-Haghighi, M. Reza Azarpazhooh, and Reza Bavarsad Shahripour.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.024 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".