Measuring quality of life in older people with cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of individuals aged 65+ with cancer will double in the next decade. Attention to quality of life (QOL) is imperative to identify relevant endpoints/outcomes in research and provide care that matches individual needs. This review summarizes recent publications regarding QOL measurement in older adults with cancer, considering implications for research and practice. RECENT FINDINGS: QOL is a complex concept and its measurement can be challenging. A variety of measurement tools exist, but only one specific to older adults with cancer. QOL is frequently measured as functional health, adverse symptoms, and global QOL, thus only capturing a portion of this concept. Yet successful QOL intervention for older adults requires drawing from behavioral and social dimensions.Growing interest in comprehensive geriatric assessment (CGA) and patient-reported outcomes (PROs) provides important opportunities for measuring QOL. Recommendations for use of CGAs and PROs in clinical practice have been made but widespread uptake has not occurred. SUMMARY: QOL is important to older adults and must be central in planning and discussing their care. It is modifiable but presents measurement challenges in this population. Various domains are associated with decline, survival, satisfaction with life, coping, and different interventions. Measurement approaches must fit with intention and capacity to act within given contexts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".