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Record W3049100131 · doi:10.1097/ccm.0000000000004584

International Survey to Establish Prioritized Outcomes for Trials in People With Coronavirus Disease 2019

2020· article· en· W3049100131 on OpenAlexaff
Nicole Evangelidis, Allison Tong, Martin Howell, Armando Teixeira‐Pinto, Julian Elliott, Luciano César Pontes Azevedo, Andrew D. Bersten, Lilia Cervantes, Derek P. Chew, Sally Crowe, Ivor S. Douglas, Ella Flemyng, Peter Horby, Jaehee Lee, Eduardo Lorca, Deena Lynch, John C. Marshall, Anne McKenzie, Sangeeta Mehta, Mervyn Mer, Andrew Conway Morris, Saad Nseir, Pedro Póvoa, Mark Reid, Yasser Sakr, Alan R Smyth, Tom Snelling, Giovanni FM Strippoli, Antoní Torres, Tari Turner, Steve Webb, Paula Williamson, Laila Woc-Colburn, Junhua Zhang, Amanda Baumgart, Sebastián Cabrera, Yeoungjee Cho, Tess E Cooper, Chandana Guha, Emma Liu, Andrea Matus González, Charlie McLeod, Patrizia Natale, Valeria Saglimbene, Andrea K. Viecelli, Jonathan C. Craig

Bibliographic record

VenueCritical Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of Toronto
FundersKyungpook National UniversityUniversity of SydneyMonash UniversityLa Trobe UniversityNational Institute for Health and Care ResearchUniversity of CambridgeWellcome Trust
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicDiseaseCoronavirusIntensive care medicineMEDLINECoronavirus InfectionsBetacoronavirusClinical trialVirologyInfectious disease (medical specialty)OutbreakInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: There are over 4,000 trials conducted in people with coronavirus disease 2019. However, the variability of outcomes and the omission of patient-centered outcomes may diminish the impact of these trials on decision-making. The aim of this study was to generate a consensus-based, prioritized list of outcomes for coronavirus disease 2019 trials. DESIGN: In an online survey conducted in English, Chinese, Italian, Portuguese, and Spanish languages, adults with coronavirus disease 2019, their family members, health professionals, and the general public rated the importance of outcomes using a 9-point Likert scale (7-9, critical importance) and completed a Best-Worst Scale to estimate relative importance. Participant comments were analyzed thematically. SETTING: International. SUBJECTS: Adults 18 years old and over with confirmed or suspected coronavirus disease 2019, their family members, members of the general public, and health professionals (including clinicians, policy makers, regulators, funders, and researchers). INTERVENTIONS: None. MEASUREMENTS: None. MAIN RESULTS: In total, 9,289 participants from 111 countries (776 people with coronavirus disease 2019 or family members, 4,882 health professionals, and 3,631 members of the public) completed the survey. The four outcomes of highest priority for all three groups were: mortality, respiratory failure, pneumonia, and organ failure. Lung function, lung scarring, sepsis, shortness of breath, and oxygen level in the blood were common to the top 10 outcomes across all three groups (mean > 7.5, median ≥ 8, and > 70% of respondents rated the outcome as critically important). Patients/family members rated fatigue, anxiety, chest pain, muscle pain, gastrointestinal problems, and cardiovascular disease higher than health professionals. Four themes underpinned prioritization: fear of life-threatening, debilitating, and permanent consequences; addressing knowledge gaps; enabling preparedness and planning; and tolerable or infrequent outcomes. CONCLUSIONS: Life-threatening respiratory and other organ outcomes were consistently highly prioritized by all stakeholder groups. Patients/family members gave higher priority to many patient-reported outcomes compared with health professionals.

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)0.076
metaresearch head score (Gemma)0.117
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 score0.076
Threshold uncertainty score0.400

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.117
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.083
GPT teacher head0.435
Teacher spread0.353 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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