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Record W3162739252 · doi:10.1161/str.52.suppl_1.p88

Abstract P88: Risk of Stroke in Hospitalized SARS-Cov-2 Infected Patients a Multinational Population-Based Study

2021· article· en· W3162739252 on OpenAlexaboutno aff
Shima Shahjouei, Soheil Naderi, Li Jiang, Durgesh Chaudhary, Christoph J. Griessenauer, Ghasem Farahmand, Stefania Mondello, Achille Cernigliaro, Faezeh Khodadadi, Venkatesh Avula, Annemarei Ranta, Martin Punter, Georgios Tsivgoulis, Ashkan Mowla, Vida Abedi, Ramin Zand

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Subarachnoid hemorrhageIntracerebral hemorrhageLogistic regressionPopulationObservational studyCerebral venous sinus thrombosisInternal medicineThrombosisEmergency medicineEnvironmental health

Abstract

fetched live from OpenAlex

The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients. Data Source: This multicenter, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). Main Outcomes and Measures: The outcome was the risk of subsequent stroke (ischemic stroke, intracranial hemorrhage, cerebral venous/sinus thrombosis). The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined study protocol. Data Extraction and Synthesis: Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Binary logistic regression was used to determine the associated factors with the outcome measure. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines. Results: We received data from 18,311 hospitalized SARS-CoV-2 patients from 77 tertiary centers in 46 regions of 11 countries until May 1 st , 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke—123(79%) ischemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centers in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1-3.5, p = 0.03) and the presence of ischemic heart disease (OR: 2.5, 95% CI:1.4-4.7, p =0·006) were predictive of stroke. Conclusion and Relevance: The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5% (pooled risk: 0.9%). The need for mechanical ventilation and the history of ischemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.018
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.309
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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