An international study of multitrial data investigating quality of life and symptoms as prognostic factors for survival in different cancer sites.
Bibliographic record
Abstract
6002 Background: The prognostic value for survival of HRQOL data derived from self-report questionnaires, has been well documented in cancer research. The objective of this study was to examine the prognostic value of HRQOL parameters for different cancer sites using one standardized and validated patient self-assessment tool. Methods: A total of 11 different cancer sites, pooled from 30 European Organisation for Research and Treatment of Cancer (EORTC) Randomized Controlled Trials (RCTs), were selected for this study. For each cancer site, univariate and multivariate Cox proportional hazard modeling was used to assess the prognostic value (p<0.05) of 15 HRQOL parameters, assessed with the EORTC QLQ-C30 at baseline before randomization, for overall survival. Models were adjusted for the parameters age, gender, distant metastasis, World Health Organization performance status and stratified by clinical study. Results: A total of 7,417 patients completed the EORTC QLQ-C30 before randomization. For brain cancer cognitive functioning (CF) (hazard ratio (HR) =0.95; p<.0001) was prognostic. For breast cancer nausea and vomiting (NV) (HR=1.17; p=0.0011) was a prognostic indicator. For colorectal cancer physical functioning (PF) (HR=0.93; p<.0001), NV (HR=1.07; p<.0001), and appetite loss (AP) (HR=1.07; p<.0001) predicted survival. For esophageal cancer PF (HR=0.88; p=0.0072) and for head and neck cancer NV (HR=1.14; p=0.0097) were prognostic. For lung cancer PF (HR=0.94; p=0.0006) and pain (HR=1.08; p<0.0001), for melanoma dyspnea (HR=1.06; p<.0001), for ovarian cancer NV (HR=1.2; p<.0001), for pancreatic cancer global QOL (HR=0.83; p=0.0073), for prostate cancer role functioning (RF) (HR=0.96; p=0.006) and AP (HR=1.07; p<.0001), and for testis cancer RF (HR=0.81; p=0.0144) were predictors of survival. Conclusions: Our findings show that different HRQOL parameters provide prognostic information for survival for patients with different tumor sites and that no single HRQOL scale can predict survival in all cancer patients. Thus, each cancer site needs careful examination and no single QOL paramenter can predict survival in all cancer diseases.
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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 | 0.242 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".