Systematic Review and Meta-analysis of the Safety of Chloroquine and Hydroxychloroquine From Randomized Controlled Trials on Malarial and Non-malarial Conditions
Bibliographic record
Abstract
Abstract Background: Despite the expectations regarding the effectiveness of chloroquine (CQ) and hydroxychloroquine (HCQ) for coronavirus disease (COVID-19) management, concerns about their adverse events have remained. Objectives: The objective of this systematic review was to evaluate the safety of CQ and HCQ from malarial and non-malarial randomized clinical trials (RCTs). Methods: The primary outcomes were the frequencies of serious adverse events (SAEs), retinopathy, and cardiac complications. Search strategies were applied to MEDLINE, EMBASE, LILACS, CENTRAL, Scopus, and Trip databases. We used random-effects model to pool results across studies and Peto one-step odds ratio (OR) for event rates below 1 %. Both-armed zero-event studies were excluded from the meta-analyses. We used the Grading of Recommendations Assessment, Development, and Evaluation system to evaluate the certainty of evidence. Results: Ninety-two RCTs were included. We found no significant difference between CQ/HCQ and control (placebo or non-CQ/HCQ) in the frequency of SAEs (OR: 0.98, 95 % confidence interval [CI]: 0.71–1.36, 25 trials, 11,605 participants, moderate certainty of evidence). No clear relationship was observed between CQ/HCQ and retinopathy (OR: 1,63, 95 % CI: -0.4–6.57, 5 trials, 344 participants, very low certainty of evidence). There was a low certainty of evidence of the effect of CQ/HCQ versus control on cardiac complications (Relative risk: 1.48, 95 % CI: 1.1–1.98, 8 trials, 5,970 participants). Conclusions: CQ and HCQ might be safe, with low frequency of SAEs on malarial and non-malarial conditions. No clear effect of their use on the incidence of retinopathy and cardiac complications was observed.The protocol for this systematic review was registered with PROSPERO (registration number : CRD42020177818)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.031 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".