A global analysis of multitrial data investigating quality of life and symptoms as prognostic factors for survival in different tumor sites
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to examine the prognostic value of baseline health-related quality of life (HRQOL) for survival with regard to different cancer sites using 1 standardized and validated patient self-assessment tool. METHODS: In total, 11 different cancer sites pooled from 30 European Organization for Research and Treatment of Cancer (EORTC) randomized controlled trials were selected for this study. For each cancer site, univariate and multivariate Cox proportional hazards modeling was used to assess the prognostic value (P< .05) of 15 HRQOL parameters using the EORTC Core Quality of Life Questionnaire (QLQ-C30). Models were adjusted for age, sex, and World Health Organization performance status and were stratified by distant metastasis. RESULTS: In total, 7417 patients completed the EORTC QLQ-C30 before randomization. In brain cancer, cognitive functioning was predictive for survival; in breast cancer, physical functioning, emotional functioning, global health status, and nausea and vomiting were predictive for survival; in colorectal cancer, physical functioning, nausea and vomiting, pain, and appetite loss were predictive for survival; in esophageal cancer, physical functioning and social functioning were predictive for survival; in head and neck cancer, emotional functioning, nausea and vomiting, and dyspnea were predictive for survival; in lung cancer, physical functioning and pain were predictive for survival; in melanoma, physical functioning was predictive for survival; in ovarian cancer, nausea and vomiting were predictive for survival; in pancreatic cancer, global health status was predictive for survival; in prostate cancer, role functioning and appetite loss were predictive for survival; and, in testis cancer, role functioning was predictive for survival. CONCLUSIONS: The current results demonstrated that, for each cancer site, at least 1 HRQOL domain provided prognostic information that was additive over and above clinical and sociodemographic variables.
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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 | 0.097 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".