Risk factors for clinician‐reported symptom clusters in patients with advanced head and neck cancer in a phase 3 randomized clinical trial: RTOG 0129
Bibliographic record
Abstract
BACKGROUND: Chemoradiotherapy has become the standard of care for head and neck squamous cell carcinoma; however, those patients often experience multiple treatment-related symptoms or symptom clusters. Two symptom clusters have been identified for this population. Little is known about the risk factors of these symptom clusters. METHODS: Subjects comprised 684 patients who were treated with concurrent chemoradiotherapy in a phase 3 randomized clinical trial. This trial compared standard fractionation radiotherapy to accelerated fractionation radiotherapy. Symptom clusters were evaluated at the end of the first and the second cycle of chemotherapy, and 3 months after the start of radiotherapy. Mixed-effect modeling was used to observe risk factors for symptom clusters. RESULTS: Race and education were independent predictors for the head and neck cluster, whereas sex and history of tobacco use were independent predictors for the gastrointestinal cluster. Primary cancer site was only significant for the head and neck cluster when other factors were not controlled: patients with oropharyngeal cancer had more severe symptoms in the head and neck clusters than did patients with laryngeal cancer. In addition, patients receiving accelerated fractionation radiotherapy experienced more symptoms of radiomucositis, pain, and nausea at 3 months after the start of radiotherapy than those receiving standard fractionation radiotherapy. CONCLUSIONS: Demographic characteristics were more predictive to symptom clusters, whereas clinical characteristics, such as cancer site and treatment arms, were more significant for individual symptoms. Knowing the risk factors will enhance the capability of clinicians to evaluate patients' risk of severe symptom clusters and to personalize management strategies.
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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 | 0.004 | 0.004 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".