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Record W3092655625 · doi:10.1093/ofid/ofaa500

Safety of Hydroxychloroquine Among Outpatient Clinical Trial Participants for COVID-19

2020· article· en· W3092655625 on OpenAlexafffund
Sarah M Lofgren, Melanie R. Nicol, Ananta Bangdiwala, Katelyn A Pastick, Elizabeth C Okafor, Caleb P Skipper, Matthew F Pullen, Nicole Engen, Mahsa Abassi, Darlisha A Williams, Alanna A Nascene, Margaret L. Axelrod, Sylvain Lother, Lauren J MacKenzie, Glen Drobot, Nicole Marten, Matthew P. Cheng, Ryan Zarychanski, Ilan S. Schwartz, Michael E. Silverman, Zain Chagla, Lauren E. Kelly, Emily G. McDonald, Todd C. Lee, Kathy Huppler Hullsiek, David R. Boulware, Radha Rajasingham

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcMaster UniversityUniversity of AlbertaLawson Health Research InstituteMcGill University Health CentreMcGill UniversityGeorge & Fay Yee Centre for Healthcare InnovationUniversity of Manitoba
FundersNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesFogarty International CenterNational Institute of Mental HealthNational Cancer InstituteNational Institutes of HealthNorthern Alberta Clinical Trials and Research CentreMcGill UniversityMcGill University Health CentreManitoba Medical Service FoundationUniversity of MinnesotaGilead SciencesDoris Duke Charitable Foundation
KeywordsHydroxychloroquineMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Clinical trialFamily medicineInternal medicineVirologyOutbreak

Abstract

fetched live from OpenAlex

BACKGROUND: Use of hydroxychloroquine in hospitalized patients with coronavirus disease 2019 (COVID-19), especially in combination with azithromycin, has raised safety concerns. Here, we report safety data from 3 outpatient randomized clinical trials. METHODS: We conducted 3 randomized, double-blind, placebo-controlled trials investigating hydroxychloroquine as pre-exposure prophylaxis, postexposure prophylaxis, and early treatment for COVID-19 using an internet-based design. We excluded individuals with contraindications to hydroxychloroquine. We collected side effects and serious adverse events. We report descriptive analyses of our findings. RESULTS: We enrolled 2795 participants. The median age of research participants (interquartile range) was 40 (34-49) years, and 59% (1633/2767) reported no chronic medical conditions. Overall 2544 (91%) participants reported side effect data, and 748 (29%) reported at least 1 medication side effect. Side effects were reported in 40% with once-daily, 36% with twice-weekly, 31% with once-weekly hydroxychloroquine, compared with 19% with placebo. The most common side effects were upset stomach or nausea (25% with once-daily, 19% with twice-weekly, and 18% with once-weekly hydroxychloroquine, vs 11% for placebo), followed by diarrhea, vomiting, or abdominal pain (23% for once-daily, 17% twice-weekly, and 13% once-weekly hydroxychloroquine, vs 7% for placebo). Two individuals were hospitalized for atrial arrhythmias, 1 on placebo and 1 on twice-weekly hydroxychloroquine. No sudden deaths occurred. CONCLUSIONS: Data from 3 outpatient COVID-19 trials demonstrated that gastrointestinal side effects were common but mild with the use of hydroxychloroquine, while serious side effects were rare. No deaths occurred related to hydroxychloroquine. Randomized clinical trials, in cohorts of healthy outpatients, can safely investigate whether hydroxychloroquine is efficacious for COVID-19. CLINICALTRIALSGOV IDENTIFIER: NCT04308668 for postexposure prophylaxis and early treatment trials; NCT04328467 for pre-exposure prophylaxis trial.

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)0.035
metaresearch head score (Gemma)0.041
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: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.183
GPT teacher head0.505
Teacher spread0.322 · 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

Citations52
Published2020
Admission routes2
Has abstractyes

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