Effect of Early Palliative Care on Quality of Life of Advanced Head and Neck Cancer Patients: A Phase III Trial
Bibliographic record
Abstract
BACKGROUND: Early palliative care (EPC) is an important aspect of cancer management but, to our knowledge, has never been evaluated in patients with head and neck cancer. Hence, we performed this study to determine whether the addition of EPC to standard therapy leads to an improvement in the quality of life (QOL), decrease in symptom burden, and improvement in overall survival. METHODS: Adult patients with squamous cell carcinoma of the head and neck region planned for palliative systemic therapy were allocated 1:1 to either standard systemic therapy without or with comprehensive EPC service referral. Patients were administered the revised Edmonton Symptom Assessment Scale and the Functional Assessment of Cancer Therapy for head and neck cancer (FACT-H&N) questionnaire at baseline and every 1 month thereafter for 3 months. The primary endpoint was a change in the QOL measured at 3 months after random assignment. All statistical tests were 2-sided. RESULTS: Ninety patients were randomly assigned to each arm. There was no statistical difference in the change in the FACT-H&N total score (P = .94), FACT-H&N Trial Outcome Index (P = .95), FACT-general total (P = .84), and Edmonton Symptom Assessment Scale scores at 3 months between the 2 arms. The median overall survival was similar between the 2 arms (hazard ratio for death = 1.01, 95% confidence interval = 0.74 to 1.35). There were 5 in-hospital deaths in both arms (5.6% for both, P = .99). CONCLUSIONS: In this phase III study, the integration of EPC in head and neck cancer patients did not lead to an improvement in the QOL or survival.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".