Abstract 14: Global Impact of the Covid-19 Pandemic on Subarachnoid Hemorrhage
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The objective was to examine whether subarachnoid hemorrhage(SAH) hospitalizations and ruptured aneurysm coiling interventions demonstrated similar declines. Methods: We conducted a global, retrospective, observational study across 6 continents, 37 countries, and 140 comprehensive stroke centers. Patients with diagnosis of SAH, ruptured aneurysm coiling interventions, COVID-19 were identified using ICD-10 codes or by prospectively maintained stroke databases. The 3-month cumulative volume, monthly volumes for SAH hospitalizations and ruptured aneurysm coiling procedures were compared for the period before (1-year and immediately before) and during the COVID-19 pandemic (March 1 to May 31, 2020). Results: There was a significant decline in SAH hospitalizations with 2,044 admissions in the 3 months immediately before and 1,585 admissions during the pandemic, representing a decline of 22.5% (95%CI, -24.3 to -20.7, p<0.0001). Embolization of ruptured aneurysms declined with 1,170 to 1,035 procedures, respectively, representing an 11.5%(95%CI, -13.5 to -9.8, p=0.002) drop. Hospitals with higher COVID-19 hospitalization burden demonstrated greater declines in SAH and ruptured aneurysm coiling compared to lower COVID-19 burden. A relative increase in coiling of ruptured aneurysms was noted in low coiling volume hospitals of 41.1% (95%CI, 32.3-50.6, p=0.008) despite a decrease in SAH admissions in this tertile. Conclusions: There was a global decrease in subarachnoid hemorrhage admissions and ruptured aneurysm embolizations during the COVID-19 pandemic. Among low-volume coiling SAH hospitals, there was an increase in the ruptured aneurysm coiling intervention. These findings in SAH are consistent with a global decrease in other emergencies such as stroke and myocardial infarction.*On behalf of the SVIN COVID-19 Collaborators
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".