Impact of Repurposed Hydroxychloroquine and Chloroquine on Cardiovascular Health During COVID-19: A Systematic Review
Bibliographic record
Abstract
The anti-malarial and immunomodulatory drug hydroxychloroquine (HCQ) was seen as a viable option for therapeutic repurposing in coronavirus illness (COVID-19). However, based on clinical research, the concentration-dependent effects of HCQ are inconclusive. Web of Science, PubMed, Embase, MEDLINE, Global Health databases and ClinicalTrials.gov were searched from December 10, 2022, to February 15, 2024. The factors used for eligibility requirements were Population, Intervention, Comparison: pre-post/placebo/standard care, retrospective data or randomized clinical studies. Modified Newcastle-Ottawa Quality Assessment Score and Cochrane criteria was used. Three retrospective studies (386 patients with a baseline and follow up ECG) and four randomized trials (283 patients: HCQ with standard care; azithromycin= 188; standard care/placebo= 95) used HCQ as treatment for hospitalized COVID-19 patients, one randomized trial examined chloroquine (CQ) (81 patients: CQ high dose= 41; low dose CQ=40) to treat hospitalized patients with severe COVID-19 and one case series (98 patients: HCQ= 10; azithromycin= 27; HCQ along with azithromycin = 61) studied HCQ as treatment for hospitalized confirmed/ suspected COVID-19 cases. Findings showed significant prolongation of QTc Interval in 12% to 93% of patients. The administration of HCQ combined with or without azithromycin requires continuous ECG surveillance to prevent risk of cardiotoxicity.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".