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 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 | 0.624 | 0.782 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".