Decrease in Stroke Diagnoses During the COVID-19 Pandemic: Where Did All Our Stroke Patients Go?
Bibliographic record
Abstract
Despite the evidence suggesting a high rate of cerebrovascular complications in patients with SARS-CoV-2, reports have indicated decreasing rates of new ischemic stroke diagnoses during the COVID-19 pandemic. The observed decrease in emergency department (ED) visits is unsurprising during this major crisis, as patients are likely to prioritize avoiding exposure to SARS-CoV-2 over addressing what they may perceive as mild symptoms of headache, lethargy, difficulty speaking, and numbness. In the central and south Texas regions where we practice, we suspect that patient admission, treatment, and discharge volumes for acute stroke treatment have decreased significantly since COVID-19-related shelter-at-home orders were issued. Symptoms of stroke are frequently noticed by a family member, friend, or community member before they are recognized by the patients themselves, and these symptoms may be going unnoticed due to limited face-to-face encounters. This possibility emphasizes the importance of patient education regarding stroke warning signs and symptoms during the current period of isolation and social-distancing. The south Texas population, already saddled with above-average rates of cardiovascular and cerebrovascular disease, has a higher stroke mortality rate compared to Texas and U.S. averages; however, the number of patients presenting to EDs with acute ischemic stroke diagnoses is lower than average. In our viewpoint, we aim to present the relative literature to date and outline our ongoing analyses of the highly affected and diverse stroke populations in San Antonio and Austin, Texas, to answer a simple question: where did all our stroke patients go?
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".