Impact of COVID-19 pandemic on stroke admissions in Qatar
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has resulted in a dramatic unexplained decline in hospital admissions due to acute coronary syndromes and stroke. Several theories have emerged aiming to explain this decline, mostly revolving around the fear of contracting the disease and thus avoiding hospital visits. AIMS: In this study, we aim to examine the impact of the COVID-19 pandemic on stroke admissions to a tertiary care centre in Qatar. METHODS: The Hamad General Hospital stroke database was interrogated for stroke admissions between September 2019 and May 2020. The number of stroke admissions, stroke subtypes and short-term outcomes was compared between the 'pre-COVID-19' period (September 2019 to February 2020) and the COVID-19 pandemic period (March to May 2020). RESULTS: We observed a significant decline in monthly admissions in March (157), April (128) and May (135) compared with the pre-COVID-19 6-month average (229) (p=0.024). The reduction in admissions was most evident in functional stroke mimics. The average admissions decreased from 87 to 34 per month (p=0.0001). Although there were no significant differences in admissions due to ischaemic stroke (IS), intracranial haemorrhage or transient ischaemic attacks between the two periods, we noted a relative decrease in IS due to small vessel disease and an increase in those due to large vessel atherosclerosis in March to May 2020. CONCLUSIONS: The decline in overall stroke admissions during the COVID-19 pandemic is most likely related to concerns of contracting the infection, evidenced mainly by a decline in admissions of stroke mimics. However, a relative increase in large vessel occlusions raises suspicion of pathophysiological effects of the virus, and requires further investigation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".