Safety and Efficacy of Hydroxychloroquine in COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: During the initial phases of the coronavirus disease 2019 (COVID-19) epidemic, there was an unfounded fervor surrounding the use of hydroxychloroquine (HCQ); however, recently, the Centers for Disease Control and Prevention (CDC) has recommended against routine use of HCQ outside of study protocols citing possible adverse outcomes. METHODS: Multiple databases were searched to identify articles on COVID-19. An unadjusted odds ratio (OR) was used to calculate the safety and efficacy of HCQ on a random effect model. RESULTS: Twelve studies comprising 3,912 patients (HCQ 2,512 and control 1400) were included. The odds of all-cause mortality (OR: 2.23, 95% confidence interval (CI): 1.58 - 3.13, P value < 0.00001) were significantly higher in patients on HCQ compared to patients on control agent. The response to therapy assessed by negative repeat polymerase chain reaction (PCR) (OR: 1.83, 95% CI: 0.50 - 6.75, P = 0.36), radiological resolution (OR: 1.98, 95% CI: 0.47 - 8.36, P value = 0.36) and the need for invasive mechanical ventilation (IMV) (OR: 1.21, 95% CI: 0.34 - 4.33, P value = 0.76) were identical between the two groups. Overall, four times higher odds of net adverse events (NAEs) were observed in the HCQ group (OR: 4.59, 95% CI 1.73 - 12.20, P value = 0.02). The measures for individual safety endpoints were also numerically lower in the control arm; however, none of these values reached the level of statistical significance. CONCLUSIONS: HCQ might offer no benefits in terms of decreasing the viral load and radiological improvement in patients with COVID-19. HCQ appears to be associated with higher odds of all-cause mortality and NAEs.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".