Short- and long-term effects of imatinib in patients hospitalised for COVID-19: A randomised trial
Bibliographic record
Abstract
Background: Earlier evidence has suggested that imatinib may improve 30-day survival in hospitalised COVID-19 patients. Objective: To study the short- and long-term effects of imatinib in hospitalised COVID-19 patients. Methods: We conducted a randomised trial in 15 Finnish hospitals. Participants were randomised between locally available options to receive standard or care (SoC) or SoC with imatinib. Imatinib dosage was 400 mg daily until discharge (max 14 days). Primary outcomes were mortality at 30 days and 1 year. Secondary outcomes included recovery, quality of life and long COVID symptoms at 1 year. We also performed a systematic review and meta-analysis of all randomised trials studying imatinib for 30-day mortality in hospitalised COVID-19 patients. Results: Between August 2021 and March 2023, we randomised 156 patients (103 in 2021, 51 in 2022 and 2 in 2023; 73 in SoC, 83 in imatinib). Among patients on imatinib, 7.2% had died at 30 days and 13.3% at 1 year and among those randomized to SoC 4.1% and 8.3% (adjusted HR 1.09, 95% CI 0.23–5.07). At 1-year, self-reported recovery occurred in 79.0% in imatinib and in 88.3% in SoC (RR 0.91, 0.78-1.06). We found no convincing difference between groups in quality of life or symptoms. Fatigue (24%), sleep issues (19%), and memory problems (17%) frequently bothered patients among 21 potential long COVID symptoms. In the meta-analysis of 4 randomised trials (732 patients), imatinib was associated with a mortality risk ratio of 0.73 (0.32–1.63; low certainty evidence). Conclusions: The evidence raises doubts regarding benefit of imatinib in reducing mortality, improving recovery and preventing long COVID symptoms in hospitalised COVID-19 patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".