Cloroquina y sus derivados en el manejo de la COVID-19: una revisión sistemática exploratoria
Bibliographic record
Abstract
Introduction: Recently, researchers from China and France reported on the effectiveness of chloroquine and hydroxychloroquine for the inhibition of SARS-CoV-2 viral replication in vitro. Timely dissemination of scientific information is key in times of pandemic. A systematic review of the effect and safety of these drugs on COVID-19 is urgently needed. Objective: To map published studies until March 25, 2020, on the use of chloroquine and its derivates in patients with COVID-19. Materials and methods: We searched on PubMed, Embase, Lilacs, and 15 registries from the World Health Organization’s International Clinical Trials Registry Platform for theoretical and empirical research in English, Spanish, Italian, French, or Portuguese until March 25, 2020, and made a narrative synthesis of the results. Results: We included 19 records and 24 trial registries (n=43) including 18,059 patients. China registered 66% (16/24) of the trials. Nine trials evaluate chloroquine exclusively and eight hydroxychloroquine. The records are comments (n=9), in vitro studies (n=3), narrative reviews (n=2), clinical guidelines (n=2), as well as a systematic review, an expert consensus, and a clinical trial. Conclusions: One small (n=26), non-randomized, and flawed clinical trial supports hydroxychloroquine use in patients with COVID-19. There is an urgent need for more clinical trial results to determine the effect and safety of chloroquine and hydroxychloroquine on COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".