Impact of COVID-19 pandemic on haemorrhagic stroke admissions: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: COVID-19 pandemic, a global health crisis, is disrupting the present medical environment. This systematic review and meta-analysis aimed to evaluate the impact of the COVID-19 pandemic on stroke hospitalisations, especially haemorrhagic stroke. METHODS: The EMBASE, PubMed, Web of Science, Elsevier, Medline, Cochrane Library and Google Scholar electronic databases were searched for all relevant studies. Two researchers independently screened the studies, extracted data and assessed the quality of the included studies. Odds ratio (OR), total events, OR and 95% CI were considered as the effect size. A fixed-effects model was used to pool the study-specific estimate. The present study was performed by using Review Manager (V.5.3.0) software. We assessed the risk of bias using the Newcastle-Ottawa Scale. RESULTS: A total of 17 studies with 14 445 cases were included. Overall, the number of stroke admissions is lower in the pandemic period versus the control period (6252 vs 8193). The difference of haemorrhagic stroke is significant, with 1233 of 6252 cases in the pandemic group and 1621 of 8193 cases in the control group. Intracerebral haemorrhage is present in 461 of 1948 cases in the pandemic group and 618 of 2734 cases in the control group. As for subarachnoid haemorrhage, the difference between the two groups is significant, with 70 of 985 cases in the pandemic group and 202 of 1493 cases in the control group. CONCLUSIONS: The number of stroke admissions is lower in the pandemic period compared with the control period. There is a higher rate of haemorrhagic stroke in the pandemic period. Subgroup analysis identifies a significant increase in the occurrence of intracerebral haemorrhage in the pandemic period. Due to limited data and the impact of a single article, the impact of COVID-19 pandemic on subarachnoid haemorrhage is unclear.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".