Navigating stroke care during the COVID-19 pandemic: A scoping review
Bibliographic record
Abstract
Abstract Background Previous viral outbreaks have highlighted implications for the management of complex health conditions. This study delves into the repercussions of the COVID-19 pandemic on stroke care, by examining evidence of shifts in healthcare utilization, the enduring effects on post-stroke recovery, and the overall quality of life experienced by stroke survivors. Methods A scoping review was conducted following the Joanna Briggs Institute Methodology for Scoping Reviews. The search strategy encompassed electronic databases (APA PsycInfo, Embase, Medline, and CINAHL). English language articles published between December 2019 and January 2022 were included, focusing on individuals who experienced a stroke during the COVID-19 pandemic. Data extraction involved identifying study characteristics and significant findings, facilitating a qualitative and narrative synthesis of the gathered evidence. Results Seven domain summaries were identified. They all described the aspects of systemic transformations in stroke care during the COVID-19 pandemic: (1) patient behavior and awareness; (2) telemedicine and remote care; (3) delays in treatment; (4) impact on healthcare resources; (5) quality of care; (6) changes in stroke severity; and (7) reduction in stroke admissions. Conclusions This study underscored the critical need to encourage swift patient response to acute stroke symptoms, by finding new avenues for treatment, mitigating hospital-related infection fears, and advocating for the establishment of centralized stroke centers. These measures are integral to optimizing stroke care delivery and ensuring timely interventions, particularly in the challenging context of a 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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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".