Trends in stroke admissions before, during and post-peak of the COVID-19 pandemic: A one-year experience from the Qatar stroke database
Bibliographic record
Abstract
BACKGROUND: Several reports document a decrease in the rates of stroke hospital admissions during the covid-19 pandemic. There is very little information whether the admission rates will change as the infection is controlled. We report on our rates of admissions before, during and following the peak of covid-19 infections in a prospective database from Qatar. METHODS AND RESULTS: The stroke admissions in the six months prior to COVID-19 pandemic averaged 229/month. There was a decrease to 157/month in March-June during the peak of the pandemic. In the 6 months following the peak, as covid-19 numbers began to decrease, the average numbers increased back to 192/month. There was an increase in severe ischemic strokes and decreased in functional recovery. The decreased admissions were mainly driven by fewer stroke mimics. Patients presenting with ischemic stroke or cerebral hemorrhage remained unchanged. CONCLUSIONS: Fewer stroke mimics presenting to the hospital can explain the fewer admissions and poor outcome at the height of the covid-19 pandemic. The continued decrease in the number of ischemic stroke and stroke mimic admissions following the pandemic peak requires more study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".