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Record W4200369359 · doi:10.1183/23120541.00552-2021

Clinical characteristics, risk factors and outcomes in patients with severe COVID-19 registered in the International Severe Acute Respiratory and Emerging Infection Consortium WHO clinical characterisation protocol: a prospective, multinational, multicentre, observational study

2021· article· en· W4200369359 on OpenAlexafffund
Luis Felipe Reyes, Srinivas Murthy, Esteban García-Gallo, Mike Irvine, Laura Merson, Ignacio Martín‐Loeches, Jordi Rello, Fabio Silvio Taccone, Robert Fowler, Annemarie B Docherty, Christiana Kartsonaki, Irene Aragão, Peter Barrett, Abi Beane, Aidan Burrell, Matthew Pellan Cheng, Christian Sandrock, José Pedro Cidade, Barbara Wanjiru Citarella, Christl A. Donnelly, S. M. L. Fernandes, Craig French, Rashan Haniffa, Ewen M. Harrison, Antonia Ho, Mark Joseph, Irfan Khan, Michelle E. Kho, Anders Benjamin Kildal, Demetrios J. Kutsogiannis, François Lamontagne, Todd C. Lee, Gianluigi Li Bassi, José W. López, Catherine Marquis, Jonathan Millar, Raul Neto, Alistair Nichol, Rachael Parke, Rui Pereira, Sergio Poli, Pedro Póvoa, Kollengode Ramanathan, Oleksa Rewa, Jordi Riera, Sally Shrapnel, Maria Silva, Andrew Udy, Timothy M. Uyeki, Steve Webb, Evert‐Jan Wils, Amanda Rojek, Piero Olliaro

Bibliographic record

VenueERJ Open Research · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of AlbertaMcGill UniversityHealth Sciences CentreCentre Hospitalier Universitaire de SherbrookeSunnybrook Health Science CentreBC Centre for Disease ControlBC Children's Hospital
FundersMedical Research CouncilForeign, Commonwealth and Development OfficePublic Health EnglandUniversity College DublinNorges ForskningsrådImperial College LondonCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchNational Institute for Health Research Health Protection Research UnitWellcome TrustHealth Research BoardBill and Melinda Gates Foundation
KeywordsMedicineInterquartile rangeMechanical ventilationIntensive care unitProspective cohort studyInternal medicineCase fatality rateEmergency medicineIntensive care medicineEpidemiology

Abstract

fetched live from OpenAlex

Due to the large number of patients with severe coronavirus disease 2019 (COVID-19), many were treated outside the traditional walls of the intensive care unit (ICU), and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or noninvasive mechanical ventilation, high-flow nasal cannula, inotropes or vasopressors. A logistic generalised additive model was used to compare clinical outcomes among patients admitted or not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median (interquartile range (IQR), 67 (55–78) years), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 (5–19) days and was longer in patients admitted to an ICU than in those who were cared for outside the ICU (12 (6–23) daysversus8 (4–15) days, p<0.0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% (5797 out of 18 831)versus39.0% (7532 out of 19 295), p<0.0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR 0.70, 95% CI 0.65–0.75; p<0.0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside an ICU.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.320
GPT teacher head0.565
Teacher spread0.245 · 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

Citations68
Published2021
Admission routes2
Has abstractyes

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