THE SIDE EFFECTS OF SORAFENIB, SUNITINIB, AND TEMSIROLIMUS AND THEIR THERAPY IN PATIENTS WITH METASTATIC RENAL-CELL CARCINOMA
Bibliographic record
Abstract
Objective: to provide a systematic review of the adverse reactions of sorafenib, sunitinib, and temsirolimus and to outline actions for their prevention and correction.Materials and methods. To provide a description of the main methods to decrease the toxicity of these drugs, the authors made a systemat- ic review of their adverse reactions, by using the publications available in the PubMed database, monographs on the medicines, and instruc- tions for their medical use. Results. The frequency of their adverse reactions varied from < 1 to 72%. Grades III—IV side effects are noted more rarely; their incidence is < 1 to 13% for sorafenib, < 1 to 16% for sunitinib, and 1 to 20% for temsirolimus. Sinitinib causes most grades III—IV adverse reactions and sofafenib does the least. However, close comparative studies of the safety of these kinase inhibitors are still lacking. Virtually all side effects can be effectively prevented and treated. Conclusion. The prevention, timely recognition, and treatment of the adverse reactions of these agents are of great importance, which allows avoidance of the unneeded dosage reduction that may result in worse therapeutic efficiency.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".