Are Family Proxies a Valid Source of Information About Cancer Patients' Quality of Life at the End-of-Life? A Literature Review
Bibliographic record
Abstract
In patients with advanced cancer, the achievement of a peaceful death through palliative care is highly desired. The preservation of quality of life (QOL) is a primary goal of palliative treatments; measures of QOL thus serve as important indicators of the treatment efficacy. Patient ratings have traditionally been viewed as the gold standard for QOL measures because the physical and emotional symptoms that influence QOL are subjective phenomena. However, the palliative patient group may experience difficulties with symptom self-report. This raises serious issues in clinical trials concerning non-response bias and generalizability of the data. To address the non-response issue, proxy informants are often elicited to act as surrogate respondents for patients with advanced disease. However, satisfactory levels of agreement between patient and proxy QOL appraisals must be demonstrated before caregivers (i.e., family members and close friends) can be deemed reliable sources of QOL assessments. This review finds that although family caregivers are not ideal sources for data involving terminally ill cancer patients' QOL, they can provide reliable accounts of patients' symptoms in some aspects. Only a modest bias has been observed in studies eliciting family proxy responses for patient QOL at the end-of-life. Future studies should provide a more comprehensive review of the factors influencing the level of concordance between patient and proxy QOL assessments.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".