A Systematic Review and Meta-Analysis of the Safety of Hydroxychloroquinein a Randomized Controlled Trial and Observational Studies
Bibliographic record
Abstract
INTRODUCTION: Hydroxychloroquine (HCQ) has recently become the focus of attention in the current COVID-19 pandemic. With an increase in the off-label use of HCQ, concern for the safety of HCQ has been raised. We, therefore, performed this systematic review to analyze the safety data of HCQ against placebo and active treatment in various disease conditions. METHODS: We searched PubMed, Embase, and Cochrane for Randomized Controlled Trials (RCTs) and Observational Studies (OSs) that evaluated HCQ for the treatment of any disease other than COVID19 in adult patients up to May 2020. We assessed the quality of the included studies using Risk of Bias 2 (for RCTs) and Newcastle-Ottawa Scale (for OSs). Data were analyzed with randomeffect meta-analysis. Sensitivity and subgroup analyses were performed to identify heterogeneity. RESULTS: A total of 6641 studies were screened, and 49 studies (40 RCTs and 9 OSs) with a total sample size of 35044 patients were included. The use of HCQ was associated with higher risks of TDAEs as compared to placebo/no active treatment [RR 1.47, 95%CI 1.03-2.08]. When HCQ was compared with active treatments, the risks of AEs [RR 0.74, 95% CI 0.63-0.86] and TDAEs were less in the HCQ arm [RR 0.57, 95% CI 0.39-0.81]. The outcomes did not differ in the sensitivity analysis. CONCLUSION: The results suggest that the use of HCQ was associated with a lower risk of AEs and TDAEs as compared to active treatment, whereas posing higher risk of TDAEs as compared to placebo.
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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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".