Can Mirtazapine Prevent Common Side Effects of Temozolomide and be Tolerated by Newly Diagnosed Glioblastoma Patients? Experience with Two Patients
Bibliographic record
Abstract
Patients newly diagnosed with glioblastoma (GBM) often experience psychological depression, which diminishes their quality of life, and correlates with shorter overall-survival times [1].The first-line chemotherapy treatment for these patients is temozolomide (TMZ), a drug with several significant side effects, including nausea, vomiting, weight loss, and fatigue [2].Mirtazapine is a Food and Drug Administration (FDA) approved antidepressant that can stimulate appetite and is occasionally used for insomnia.While there are, to date, no data from GBM patients, mirtazapine has shown anti-nausea and anti-vomiting activity in thoracic cancer patients receiving chemotherapy [3].The potential of mirtazapine to counter the side effects of TMZ makes the use of mirtazapine in GBM patients of substantial clinical interest.Here we present pilot data from two patients who were clinically depressed post-surgery and prescribed mirtazapine before starting TMZ.Assessments of adherence, adverse events attributable to mirtazapine, depression, sleep and appetite disturbance, and frequency/intensity of nausea and vomiting were made at four-and eight-week follow-up exams.Results show good adherence to both mirtazapine and TMZ, recovery from depression, little disturbance of sleep or appetite, and few episodes of nausea or vomiting.No adverse events attributable to mirtazapine were observed.These findings justify a follow-on Phase II study on the potential of mirtazapine to mitigate the side effects of TMZ in newly diagnosed GBM 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".