Bibliographic record
Abstract
We read with interest the Letter to the Editor titled “Can Colchicine as an Old Anti-Inflammatory Agent Be Effective in COVID-19?” by Nasiripour et al.,1 in which the authors stated that only 1 phase 3 multicenter, randomized, double-blind, placebo-controlled multicenter study has been assigned to clinicaltrials.gov by the Montreal Heart Institute to investigate the efficacy and safety of colchicine in adult patients diagnosed with COVID-19.2 In Argentina, we started COLCOVID Trial to test the effects of colchicine on moderate-/high-risk hospitalized COVID-19 patients with the aim of reducing mortality. This study is a phase 3 multicenter, controlled trial; our protocol was published in ClinicalTrial.gov on March 31, 2020.3 Therefore, we would like to affirm that, besides the COLCORONA Trial, there is another study ongoing focused on colchicine in COVID-19. We appreciate the opportunity to express our comment following the recent publication of the potential role of colchicine in SARS-CoV-2 infection. Pablo Corral reports honoraria for lectures from Amgen, Sanofi, AstraZeneca, and PTC; research grants from Amgen and Genzyme, and personal fees for consultancy from Amgen, Novartis, and Sanofi. Rafael Diaz reports research grants from Sanofi, DalCor, Population Health Research Institute, Duke Clinical Research Institute, the TIMI group, Amgen, Cirius, Montreal Health Innovations Coordinating Center, and Lepetit and personal fees from Amgen and Cirius.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".