Short- and long-term effects of imatinib in patients hospitalised for COVID-19 infection: A randomised controlled trial
Bibliographic record
Abstract
Abstract We report the short- and long-term results of the SOLIDARITY Finland on mortality and other patient-important outcomes in patients hospitalised for COVID-19. Between 08/2021 and 03/2023, we randomised 156 patients in 15 hospitals. In the imatinib group, 7.2% of patients had died at 30 days and 13.3% at 1 year and in the standard of care group 4.1% and 8.3% (adjusted HR at 30 days 1.09, 95% CI 0.23–5.07). In a meta-analysis of randomised trials of imatinib versus standard of care (n=732), allocation to imatinib was associated with a mortality risk ratio of 0.73 (95% CI 0.32–1.63). At 1-year, self-reported recovery occurred in 79.0% in imatinib and in 88.3% in standard of care (RR 0.91, 95% CI 0.78-1.06). Of the 21 potential long COVID symptoms, patients often reported moderate or major bother from fatigue (24%), sleeping problems (19%) and memory difficulties (17%). We found no convincing difference between imatinib and standard of care groups in quality of life or symptom outcomes. The evidence raises serious doubts regarding the benefit of imatinib in reducing mortality, improving recovery, and preventing potential long COVID symptoms when given to patients hospitalised for COVID-19.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".