Health utilities in cancer patients
Bibliographic record
Abstract
BACKGROUND: Cost-utility analysis (CUA) is becoming more commonly used in healthcare decision-making. CUA uses the quality-adjusted life-years (QALY) metric, which combines the length of life with the health-related quality of life (HRQoL). Most QALY-measuring instruments were validated for general populations. For patients with cancer, the perception of their health state is different and may vary by the type of cancer considered. In Quebec, no preference weights for QALY have been developed, neither for the general population nor particular subpopulations. METHODS/DESIGN: This survey is a prospective, longitudinal cohort study. The study objectives are: to assess the extent of difference in health utilities between the general population and patients with breast or colorectal cancer; to develop a QALY preference weights dataset for patients with cancer; and to perform "mapping" with different HRQoL questionnaires by correlating the SF-6Dv2 with the EQ-5D-5L, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30, and functional assessment of cancer therapy - general questionnaires. Data will be collected via a self-administered online survey. Patients' health utilities will be measured within 2 days before the beginning of a chemotherapy treatment cycle and about 8 days after the start of the chemotherapy. Health utilities will be measured by a hybrid method using the time-trade-off and discrete choice experiment methods. ETHICS AND DISSEMINATION: The proposed research was reviewed and approved by the Institutional Research Ethics Review Boards of the CHUS. We will disseminate our study findings through peer-reviewed publications and conference presentations.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".