The Influence of Proxy Perspective on Patient-Proxy Agreement in the Evaluation of Health-Related Quality of Life
Bibliographic record
Abstract
AIMS: There are situations in which patient self-reported health-related quality of life must be substituted by proxy assessments. Pickard and Knight recently delineated 2 proxy perspectives that may influence the nature of proxy measurements: the "proxy-patient" (the proxy's assessment from the patient's perspective) and the "proxy-proxy" perspective (the proxy's own assessment of the patient). They argued that the "proxy-patient" perspective would be " optimally consistent with the patient's view." The purpose of this study was to evaluate these to proxy perspectives in terms of patient-proxy bias and agreement. METHODS: The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 was administered to 224 cancer patients and their proxies (ie, significant others), who were randomly assigned to 1 of the 2 proxy conditions. Differences in patient-proxy bias and agreement were evaluated using t tests, percentage agreement, and intraclass and Pearson correlation coefficients. RESULTS: Small yet significant amounts of patient-proxy bias were found in both conditions, with patients reporting higher levels of functioning and lower symptoms levels than proxies. However, no significant differences in bias were observed between the conditions. Significantly better agreement on the role and cognitive functioning scales was found in the "proxy-proxy" condition, and for the diarrhea scale in the "proxy-patient" condition. CONCLUSIONS: There is some indication that several of the European Organization for Research and Treatment of Cancer functional scales may perform better when using the "proxy-proxy" perspective. However, no compelling evidence was found for clear superiority of either proxy perspective. These results deserve further study, as they are not entirely consistent with the pattern of agreement implied by Pickard and Knight.
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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 | 0.187 | 0.350 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".