Impact of aprepitant on emesis control, dose intensity, and recurrence-free survival in a population-based cohort of head and neck cancer patients receiving high-dose cisplatin chemotherapy
Bibliographic record
Abstract
BACKGROUND: Standard care for locally advanced head and neck cancer (HNC) patients consists of high-dose cisplatin with radiation to prolong recurrence-free survival (RFS). However, poorly controlled emesis can compromise optimal dose intensity (DI) and affect disease control. OBJECTIVE: To evaluate the impact of aprepitant on emesis control, DI, and RFS. METHODS: HNC patients treated at the British Columbia Cancer Agency were analyzed. Kaplan-Meier method and adjusted Cox proportional hazard models were used to evaluate RFS in aprepitant users. To control for selection bias, a propensity score analysis was conducted. RESULTS: A total of 192 HNC patients were included: 141 received aprepitant prophylaxis. The aprepitant-treated and untreated groups were comparable in mean age (56.3 vs 58.1 years), male gender (82.3% vs 86.3%), tumor location, and number of metastatic sites. However, more patients in the aprepitant group than in the untreated group had surgically resectable disease (31.2% vs 15.7%, respectively) and better performance status (ECOG 0/1, 87.9% vs 76.4%). Less emesis was reported in the aprepitant group (21.3% vs 28.0%). Patients in the treated group were also more likely to complete 3 cycles of high-dose cisplatin (OR, 2.3; P = .03). The propensity score adjusted Cox regression analysis suggested a reduced risk of disease recurrence in patients who received aprepitant (HR, 0.47; 95% CI, 0.17- 1.28). LIMITATIONS: Potential confounders such as other diseases or treatments that may have influenced the presence of nausea/emesis symptoms. CONCLUSION: Aprepitant contributed to improved emesis control, enhanced DI, and better adherence to cisplatin chemotherapy.
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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 | 0.003 | 0.001 |
| 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".