Colchicine treatment can improve outcomes of coronavirus disease 2019 (COVID‐19): A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Currently, there is no widely acceptable and proven effective treatment for coronavirus disease 2019 (COVID‐19). Colchicine has been shown to offer a benefit in reducing the inflammation in several inflammatory diseases. This study aims to analyze the efficacy of colchicine administration and outcomes of COVID‐19. We systematically searched the PubMed and Europe PMC database using specific keywords related to our aims until January 29, 2021. All articles published on COVID‐19 and colchicine treatment were retrieved. The quality of the study was assessed using the Newcastle–Ottawa Scale (NOS) tool for observational studies and Revised Cochrane risk‐of‐bias tool for randomized trials (RoB 2) for clinical trial studies. Statistical analysis was done using Review Manager 5.4 software. A total of eight studies with 5778 COVID‐19 patients were included in this meta‐analysis. This meta‐analysis showed that the administration of colchicine was associated with improvement of outcomes of COVID‐19 [OR 0.43 (95% CI 0.34–0.55), p < 0.00001, I2 = 0%, fixed‐effect modelling] and its subgroup which comprised of reduction from severe COVID‐19 [OR 0.44 (95% CI 0.31–0.63), p < 0.00001, I2 = 0%, fixed‐effect modelling] and reduction of mortality rate from COVID‐19 [OR 0.43 (95% CI 0.32–0.58), p < 0.00001, I2 = 0%, fixed‐effect modelling]. Our study suggests the routine use of colchicine for treatment modalities of COVID‐19 patients. More randomized clinical trial studies are still needed to confirm the results from this study.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.006 | 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".