Assessing the impact of aprepitant on response to dose-intensive cyclophosphamide, etoposide, and cisplatin (DICEP)
Bibliographic record
Abstract
BackgroundDespite evidence demonstrating the effectiveness of aprepitant for chemotherapy-induced nausea and vomiting (CINV), its use in stem cell transplant settings across Canada is not standard. While pharmacokinetic data exists, the clinical significance of cytochrome P450 3A4 (CYP 3A4) inhibition of cyclophosphamide by aprepitant is unclear. Reduced activation of cyclophosphamide may reduce the effectiveness of dose-intensive cyclophosphamide, etoposide, and cisplatin (DICEP).ObjectivesTo compare response rates to DICEP in patients with Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL) in the presence and absence of aprepitant.MethodsA retrospective review of patients who received full-dose DICEP for relapsed/refractory HL or DLBCL between June 1995 and September 2018 at the Foothills Medical Centre (FMC) in Calgary, Alberta, Canada was conducted. Descriptive statistics were used to assess response rate, as defined by the 2007 International Working Group response criteria.ResultsOf the 218 patients included in this study, 87.6% of patients in the control group and 88.5% of patients in the aprepitant group responded to DICEP (difference 0.025 [95% CI, -0.066 to 0.114], p = 0.827). Univariate analyses for age, sex, type of cancer, stage of cancer, number of prior relapses, and relapse status were not significant. No significant differences were observed for secondary outcomes.ConclusionResponse rates to DICEP in relapsed/refractory HL and DLBCL patients were similar regardless of aprepitant use. Considering these results and the effectiveness of aprepitant in CINV, its addition to standard antiemetic therapy in patients receiving DICEP should be given strong consideration in the transplant setting.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".