A concise description of cardioprotective strategies in doxorubicin-induced cardiotoxicityThis article is one of a selection of papers published in a special issue celebrating the 125th anniversary of the Faculty of Medicine at the University of Manitoba.
Bibliographic record
Abstract
Doxorubicin (Dox) is frequently used as a frontline chemotherapeutic agent against a variety of cancers. Tremendous progress has been made on its optimal usage over the last 40 years. However, cardiotoxicity still remains a major concern. The great promise in this matter is that the mechanisms leading to antitumor activity appear to be different from those leading to Dox-induced cardiomyopathy. In this regard, various cardioprotective agents have been discussed. Attention should be drawn to probucol, a lipid-lowering agent with potent antioxidant properties, which provides complete protection against Dox-induced cardiomyopathy in rats without interfering with the antitumor properties of Dox in an experimental setting. Clinical trials employing Dox therapy in combination with probucol are needed to determine whether the outstanding findings in animal experiments can be extrapolated to clinical results. We have much further to go before the establishment of cancer therapies without any risk of cardiac side effects.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".