Managing chemotherapy-induced nausea and vomiting in head and neck cancer patients receiving cisplatin chemotherapy with concurrent radiation
Bibliographic record
Abstract
BACKGROUND: The purpose was to retrospectively examine the anti-emetic regimens prescribed for prophylaxis of chemotherapy-induced nausea and vomiting (CINV) for head and neck cancer patients receiving moderate- or high-emetogenic chemotherapy (MEC/HEC) along with concurrent radiation treatment at an outpatient ambulatory care center to determine the efficacy of anti-emetics prescribed. METHODS: Consecutive patients with head and neck cancers who initiated cisplatin chemotherapy with concurrent radiation treatment between January 2013 and June 2015 were investigated. Patients' anti-emetic use and occurrence of CINV was extracted from available clinical documentation. Patients were divided into two cohorts: CISPL-HIGH (n=161), and CISPL-WEEKLY (n=38). RESULTS: A total of 199 head and neck cancer patients (158 male, 41 female) were included in the analysis (mean age =59 years). In the CISPL-HIGH cohort, 33 males (26%) and 16 females (49%) experienced CINV. In the CISPL-WEEKLY cohort, four males (13%) and two females (25%) experienced CINV. Nausea occurred in 71 patients (62 HEC and 9 MEC). The odds of achieving complete response (no nausea or vomiting) were 3.5 (P<0.0016) times more likely for patients receiving MEC. Overall, the complete response rate for the prophylaxis in MEC and HEC was 61% and 31%, respectively. Anti-emetic changes occurred in 34% and 11% of patients receiving HEC and MEC, respectively. CONCLUSIONS: In the current study CINV control for patients receiving HEC was sub-optimal. Changes to our prophylactic antiemetic regimens may help improve patient outcomes.
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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) | 0.001 | 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".