Health-Related Quality of Life Parameters As Prognostic Factors in a Nonmetastatic Breast Cancer Population: An International Multicenter Study
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to evaluate whether baseline health-related quality of life (HRQOL) parameters are prognostic factors for survival in locally advanced breast cancer patients. Although the literature highlights the important role of HRQOL parameters in predicting survival in advanced metastatic disease, little evidence exists for earlier stages. PATIENTS AND METHODS: The overall sample consisted of 448 patients randomly assigned to receive cyclophosphamide, epirubicin, and fluorouracil versus epirubicin, cyclophosphamide, and granulocyte colony-stimulating factor. Patients were enrolled in 12 countries. HRQOL baseline scores were assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30. The Cox proportional hazards regression model was used for both univariate and multivariate analyses of survival. In addition, a bootstrap resampling technique was used to assess the stability of the outcomes. Bootstrap results were then applied for model averaging purposes as a means to account for the observed model selection uncertainty. RESULTS: The final multivariate model retained inflammatory breast cancer (T4d) as the only factor predicting overall survival (OS) with a hazard ratio of 1.375 (95% CI, 1.027 to 1.840; P =.03). The presence of inflammatory breast cancer lowers the median survival time from 6.6 to 4.2 years (36% reduction). None of the preselected HRQOL variables were prognostic for OS or disease-free survival, in either the univariate or multivariate analysis. CONCLUSION: Our findings suggest that baseline HRQOL parameters have no prognostic value in a nonmetastatic breast cancer population.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".