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Record W3021463756 · doi:10.1101/2020.05.06.20093237

How robust are the results of one of the first positive trials exploring hydroxychloroquine for treatment of COVID-19?

2020· preprint· en· W3021463756 on OpenAlexaff
Ronald Chow, Sameer Elsayed, Michael Lock

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsWestern University
Fundersnot available
KeywordsHydroxychloroquineCoronavirus disease 2019 (COVID-19)Clinical trialPandemicClinical endpointMedicineIndex (typography)Robustness (evolution)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineComputer science

Abstract

fetched live from OpenAlex

Abstract An outbreak of a novel human coronavirus infection emerged in Wuhan, China in December 2019. Two months later, the World Health Organization (WHO) announced SARS-CoV-2 as the name for the new virus and COVID-19 for the associated illness. On March 12, 2020, the WHO officially declared COVID-19 as a pandemic. The scientific community has raced to find effective therapeutic agents against the virus. Gautret et al 2020 is among one of the first purportedly positive trials of hydroxychloroquine for the treatment of COVID-19. However, it is imperative that a thorough analysis and understanding of trial data be undertaken prior to making claims about safety and efficacy. Our group assessed the statistical robustness of the trial using the Fragility Index (FI). The FI provides a numerical quantification of a clinical trial’s conclusions. The index is based on iterative statistical calculations to determine the minimum number of events within a trial that would theoretically need to change from positive to negative in order for the trial’s endpoint to convert from significant to non-significant; the higher the index, the more statistically robust the study results. For the Gautret et al trial, one endpoint had an FI of 1, two had indices of 2, and another had an index of 4. The primary endpoint of viral clearance on day 6 had an FI of 4. This indicates that if 4 events were to change from positive to negative, the conclusion of the trial would become mathematically non-significant. This index is comparable to many other published trials of established agents; the median FI across the reported literature appears to be 2. In conclusion, the trial results reported by Gautret et al are statistically robust, assuming that data quality is not compromised; however, the study was an open-label trial with non-homogenous groups, with analysis conducted per-protocol. Additionally, SARS-CoV-2 Reverse Transcriptase-PCR (RT-PCR) testing was not conducted in a systematic way amongst the two groups. Further analyses of this trial and future trials of antiviral agents with potential activity against SARS-CoV-2 should be performed with complementary epidemiologic and statistical techniques to determine whether the trial’s results are clinically important and/or should be explored in depth. Given the statistically robust results reported by Gautret et al, despite the study’s inherent methodological and analytical flaws, hydroxychloroquine should be studied as a potential agent against COVID-19 in larger clinical trials.

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)0.009
metaresearch head score (Gemma)0.478
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.469
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.478
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.859
GPT teacher head0.544
Teacher spread0.315 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations4
Published2020
Admission routes1
Has abstractyes

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