Sunitinib-Induced Elevation of Mean Corpuscular Volume (MCV)—Exploring Its Possible Clinical Relevance in Cancer Patients
Bibliographic record
Abstract
Sunitinib is a broad-spectrum multitargeted tyrosine kinase inhibitor mainly used as second-line therapy for non-resectable gastrointestinal stromal or first-line treatment option of metastatic renal cell carcinoma (mRCC), and as an “off-label” option in pediatric oncology. It has been previously reported that sunitinib elevates the mean corpuscular volume of erythrocytes (MCV) in treated subjects. The aim of this study was to assess time-dependent changes of this effect and evaluate its possible clinical relevance. In this study, 179 adult and 21 pediatric patients with solid tumors treated with sunitinib were retrospectively analyzed. The laboratory and treatment-related data were collected for each treatment period. The regression model with a broken-line relationship was used to fit time dependence of the MCV. In the adult group, the MCV was increasing during the first 21.6 weeks (median) of treatment in a median level of 99.8 fL, where it stabilized. MCV increase was faster in the patients who suffered from treatment-related adverse events (21.3 vs. 24.6 weeks, p = 0.010). In the pediatric cohort, the MCV dynamics were similar to adults. In conclusion, MCV changes during sunitinib treatment in pediatric and adult patients may be of clinical utility in monitoring sunitinib treatment course.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".