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Record W4400332944 · doi:10.1016/j.hrtlng.2024.06.015

Stroke in critically ill patients with respiratory failure due to COVID-19: Disparities between low-middle and high-income countries

2024· article· en· W4400332944 on OpenAlexaff
Denise Battaglini, Thu‐Lan Kelly, Matthew Griffee, Jonathon P. Fanning, Lavienraj Premraj, Glenn Whitman, Diego Bastos Porto, Rakesh C. Arora, David Thomson, Paolo Pelosi, Nicole White, Gianluigi Li Bassi, Jacky Y. Suen, John F. Fraser, Chiara Robba, Sung‐Min Cho

Bibliographic record

VenueHeart & Lung · 2024
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsMcMaster University Medical CentreAlberta Health ServicesRoyal Columbian HospitalUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecHamilton General HospitalMontreal Heart InstituteUniversity of British ColumbiaToronto General HospitalSt. John’s Health Sciences CentreLondon Health Sciences CentreMcGill University Health CentreHôpital du Sacré-Cœur de MontréalVancouver Infectious Diseases CentreFoothills Medical CentreUniversity of CalgaryPrincess Margaret Cancer CentreSt. Boniface Hospital
FundersAdvance QueenslandPrince Charles Hospital FoundationSapienza Università di RomaQueensland GovernmentQueensland HealthUniversity of QueenslandBill and Melinda Gates FoundationPennsylvania State UniversityNorthwestern UniversityInfectious Diseases Society of AmericaEuropean CommissionWorld Health OrganizationWeill Cornell Medical CollegeUniversity of Pennsylvania
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Critically illStroke (engine)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Respiratory failureIntensive care medicineBetacoronavirusRespiratory systemEmergency medicineInternal medicineVirology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.284
Teacher spread0.275 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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