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Record W3044843675 · doi:10.1101/2020.07.17.20155218

ISARIC COVID-19 Clinical Data Report: Final report January 2020 – January 2023

2020· preprint· en· W3044843675 on OpenAlexaff
J. Kenneth Baillie, Joaquín Baruch, Abi Beane, Lucille Blumberg, Fernando A. Bozza, Tessa Broadley, Aidan Burrell, Gail Carson, Barbara Wanjiru Citarella, Jake Dunning, Loubna Elotmani, Noelia García Barrio, Jean‐Christophe Goffard, Matthew Hall, Madiha Hashmi, Peter Horby, Waasila Jassat, Christiana Kartsonaki, Bharath Kumar Tirupakuzhi Vijayaraghavan, Pavan Kumar Vecham, Cédric Laouenan, Samantha Lissauer, Ignacio Martín‐Loeches, France Mentré, Ben Morton, Daniel Munblit, Nikita Nekliudov, Alistair Nichol, David S. Y. Ong, Prasan Kumar Panda, Miguel Pedrera‐Jiménez, Michelle A. Petrovic, Nagarajan Ramakrishnan, Grazielle Viana Ramos, Claire Roger, Amanda Rojek, Oana Săndulescu, Malcolm G. Semple, Pratima Sharma, Sally Shrapnel, Louise Sigfrid, Benedict Sim Lim Heng, Budha Charan Singh, Emily C. Somers, Anca Streinu‐Cercel, Fabio Silvio Taccone, Jia Wei, Evert‐Jan Wils, Xin Ci Wong, Piero Olliaro, Laura Merson

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsHumber River Regional Hospital
Fundersnot available
KeywordsMedicinePreparednessMalaiseCohortOutbreakPediatricsPublic healthCoronavirus disease 2019 (COVID-19)PneumoniaFamily medicineEmergency medicineDiseaseInternal medicineInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

Abstract ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 18th and final report, is a part of a series published over 3 years. Data have been entered for 945,317 individuals from 1807 partner institutions and networks across 76 countries. The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 10 January 2023, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 845,291 cases who meet eligibility criteria for this report, selected findings include: Median age of 57 years, with an approximately equal (50/50) male:female sex distribution 29% of the cohort are at least 70 years of age, whereas 6% are 0-19 years of age The most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time The five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports Age-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above 15% of patients with relevant data available (845,291) were admitted at some point during their illness into an intensive care unit (ICU), which has decreased from 19% during the 3 years of ISARIC reporting Antibiotic agents were used in 37% of patients for whom relevant data are available (802,241), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (64,669), 90% received antibiotics Use of corticosteroids was reported in 25% of all patients for whom data were available (809,043); in ICU/HDU admitted patients with data available (64,713), 71% received corticosteroids Outcomes are known for 762,728 patients and the overall estimated case fatality ratio (CFR) is 22% (95%CI 21.9-22), rising to 36% (95%CI 35.6-36.1) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.549
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.549
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.024
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0010.001

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 head0.402
GPT teacher head0.551
Teacher spread0.149 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations41
Published2020
Admission routes1
Has abstractyes

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