Prognostic factor analysis of health-related quality of life data in cancer: a statistical methodological evaluation
Bibliographic record
Abstract
A significant body of research exists in oncology to identify and evaluate prognostic factors, historically focused on histology, clinical stage and laboratory parameters. Recent evidence suggests that patient self-reported health-related quality-of-life (HRQOL) data provide additional prognostic information. A review by Gotay et al. of published prognostic analyses reports on the usefulness of patient-reported outcomes (PROs), including HRQOL, in predicting survival in cancer patients in clinical trials. An impressive number of studies have found a positive relationship that supports an independent association between HRQOL and survival. However, due to the considerable diversity in, for example, patient groups, types of HRQOL measures used and analytical strategies, current evidence is far from conclusive. This paper examines the statistical research methods employed, discusses key issues for HRQOL prognostic factor-analysis parameters and proposes recommendations for future outcome research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.140 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".