Retrospective Analysis of the Efficacy of Antiemetic Prophylaxis in Different Preparative Regimens for Autologous and Allogeneic Bone Marrow Transplantation
Bibliographic record
Abstract
Chemotherapy-induced nausea and vomiting (CINV) is of particular concern for patients undergoing bone marrow transplantation (BMT) given the highly emetogenic agents used. We conducted a retrospective observational study to verify the efficacy of our different prophylactic CINV prevention regimens for different types of BMT performed at our center. We looked at 78 autologous and 38 allogeneic BMT patients hospitalised between august 2007 and july 2009. Patients received our standard antiemetic prophylaxis tailored to the preparative regimen. Most patients received a 5-HT3 antagonist and dexamethasone as prophylaxis and prochlorperazine for breakthrough nausea. We measured emesis, nausea and use of breakthrough antiemetics on a daily basis. Results of the overall absence of emesis, nausea and non-use of breakthrough antiemetics throughout the entire duration of the BMT process are given in the following table.Table 1Control of nausea and emesis by preparative regimenBEAMHD-MelFlu-CyTBI-CyBu-CyFlu-Bu-ATGAbsence of vomiting59.6%29.0%38.5%0%33.3%66.7%Absence of nausea25.5%6.5%15.4%0%0%50%Non-use of breakthrough agents25.5%12.9%15.4%0%0%50% Open table in a new tab In the daily evaluations, patients receiving the BEAM regimen and the high-dose melphalan (HD-Mel) were particularly at risk of emesis immediately following the melphalan. Control of CINV was approximately 90% before administration of melphalan and dropped to 45% on the following days. The study included only a small number of patients in the allogeneic BMT setting. This group was then divided in even smaller cohorts for each different regimen making it harder to reach conclusions. However, all groups showed a low incidence of complete control of CINV. The use of high dose cyclophosphamide has a preparative regimen is associated with very poor nausea control. This retrospective case study demonstrates the need to reassess periodically the effectiveness of our prophylactic regimens for emesis in the BMT setting and to make the appropriate changes to improve our supportive care. Our center has changed our prophylactic CINV prevention regimens and aprepitant (Emend®) has been added to some of BMT regimens even if it has not yet received approval for this indication. Ongoing trials are assessing its efficacy but early trials have successfully proven its safety. A second study is planned at our center to evaluate if these modifications will improve the control of nausea and emesis in our BMT patients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".