A Taxonomy of the Uses of Health-Related Quality-of-Life Instruments in Cancer Care and the Clinical Meaningfulness of the Results
Bibliographic record
Abstract
OBJECTIVES: To propose a taxonomy of psychometrically based, health-related quality-of-life instruments related to three levels of decision-making of health care: the macro, meso and micro levels. The choice of appropriate health-related quality-of-life instruments for each level of desired decision making in various clinical settings is illustrated. A secondary objective was to describe solutions for some of the difficulties inherent in the interpretation of the results of health-related quality-of-life assessment. DESIGN: The three main levels of clinical decision making are listed and the instruments used most frequently in cancer clinical trials are reviewed from the medical literature. PROPOSALS: Generic and utility-based instruments are likely to be the most valuable at the macro level of decision making, whereas condition-specific, disease-specific, and situation-specific instruments are most useful for decision making at the meso and micro levels. A determination of the proportions of patients who have reached a meaningful change in health-related quality-of-life scores (eg, > or =10 for scales of 1-100) over a standard period is a rational approach to interpreting the significance of changes in scores. CONCLUSIONS: Awareness of the level of decision making that is involved in the clinical assessment of health-related quality of life can be helpful in choosing instruments that are appropriate for various clinical settings. Some of the difficulties in interpreting the meaning of changes in health-related quality-of-life scores can be overcome by comparing the proportions of patients who have achieved a preset magnitude of change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.204 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".