Role of colchicine in the management of COVID-19 patients: A meta-analysis of cohort and randomized controlled trials
Bibliographic record
Abstract
Background: Colchicine is well known drug for the treatment of acute gout. Recently, it has also been used in the management of COVID-19 patients. Aim: The aim of current study is to find out the role of colchicine in COVID-19 patients. Material & methods: The relevant studies were searched in PubMed/Medline, Google scholar and clinical trail.gov.com till inception and sorted based on the inclusion and exclusion criteria. The quality assessment of studies were done using Newcastle Ottawa Quality Assessment Scale. The pooled estimate was calculated as odd ratio and pooled prevalence with 95% confidence interval. A random effect model was used and publication bias was assessed qualitatively by trim and fill method. Results: Out of 38 studies, a total of 6 studies were found relevant for the analysis containing 1146 patients (705 males and 441 females). The pooled odd ratio was found to be 0.35 [0.23, 0.53] which indicate significance reduction of mortality in colchicine group as compared to non-colchicine group. The pooled prevalence of the patients treated with colchicine were found to be significant [0.11(0.03, 0.24)]. The heterogeneity among studies was also found to be low (I2 = 11%). However, funnel plot has indicated the involvement of publication bias [Egger: bias = 10.168291 (95% CI = 5.042044 to 15.294537) P = 0.0053]. Conclusion: Colchicine might be helpful in reduction of mortality in the management of COVID-19 patients. However, further studies are required to confirm its exact role.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".