Can Colchicine as an Old Anti‐Inflammatory Agent Be Effective in COVID‐19?
Bibliographic record
Abstract
A pneumonia of unknown source was first reported to the World Health Organization Country Office from Wuhan, China, on December 31, 2019. Analysis of the samples obtained from the lower respiratory tract confirmed a novel coronavirus, which is now known as coronavirus disease 2019 (COVID-19). On March 11, 2020, the World Health Organization stated that COVID-19 was a pandemic disease with a mortality rate of about 3.7%. Recently, several studies have reported that a subgroup of patients with intense COVID-19 could have suffered from a cytokine release syndrome (CRS). 2 CRS is a potentially life-threatening toxicity with an initial increase of tumor necrosis factor- (TNF-), followed by an increase in interleukin (IL)-1, IL-2, IL-6, IL-8, IL-10, and interferon (IFN- ). 3 A cytokine profile was detected in COVID-19, including increased IL-2, IL-7, IFN- , granulocyte colony-stimulating factor, monocyte chemoattractant protein 1, macrophage inflammatory protein 1-, and TNF-. In addition, increased ferritin and IL-6 were introduced as predictors of fatality in COVID-19. All reported data could be considered as proof, confirming the activation of inflammation processes and the occurrence of CRS in critical patients with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".