Risk versus Benefit of Using Hydroxychloroquine to Treat Patients with COVID-19
Bibliographic record
Abstract
Hydroxychloroquine (HCQ), also known by its trade name Plaquenil®, has been used for over 50 years as a treatment for malaria, systemic lupus erythematosus, and rheumatoid arthritis. As the COVID-19 pandemic emerged in the United States and globally in early 2020, HCQ began to garner attention as a potential treatment and as prophylaxis against COVID-19. Preliminary data indicated that HCQ as well as chloroquine (CQ) possessed in vitro antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early clinical data from China and France reported that HCQ and CQ were associated with viral load reduction and clinical improvement in patients with COVID-19 compared to control groups; however, an overwhelming number of randomized controlled trials, meta-analyses, and systematic reviews have since concluded that HCQ used alone, or in combination with azithromycin (AZ), provides no mortality or time-to-recovery benefit in hospitalized patients with COVID-19. Additionally, these same trials reported adverse events including cardiac, neuropsychiatric, hematologic, and hepatobiliary manifestations in patients with COVID-19 whom had been treated with HCQ. This review article summarizes the available data pertaining to the adverse events associated with HCQ use, alone or in combination with azithromycin, in patients with COVID-19 in order to fully assess the risk versus benefit of treating COVID-19 patients with these agents. The results of this review lead us to conclude that the risks of adverse events associated with HCQ use (with or without AZ) outweigh the potential clinical benefits and thus recommend against its use in the treatment or prevention of COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".