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 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 | 0.000 | 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) | 0.001 | 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".