Development of consensus-based considerations for use of adult proxy reporting: an ISOQOL task force initiative
Bibliographic record
Abstract
AIMS: Many large-scale population-based surveys, research studies, and clinical care allow for inclusion of proxy reporting as a strategy to collect outcomes when patients are unavailable or unable to provide reliable self-report. Prior work identified an absence of methodological guidelines regarding proxy reporting in adult populations, including who can serve as a proxy, and considerations for data collection, analysis, and reporting. The primary objective of this work by the ISOQOL Proxy Task Force was to review documents and clinical outcome assessment measures with respect to proxy reporting and to develop, through consensus, considerations for proxy reporting. METHODS: We assembled an international group with clinically relevant and/or methodological expertise on proxy use in adult populations. We conducted a targeted review of documentation based on regulatory, non-regulatory, professional society, and individual measure sources. Using a standardized collection form, proxy-related information was extracted from each source including definitions of a proxy, characteristics of a proxy, domains addressable or addressed by a proxy, and observer-reporting. RESULTS: The definition of proxy was inconsistent across 39 sources, except regulatory documents which defined a proxy as a person other than the patient who reports on an outcome as if she/he were the patient. While proxy report was discouraged in regulatory documentation, it was acknowledged there were instances where self-report was impossible. Many documentation sources indicated proxies would be well-justified in certain contexts, but did not indicate who could act as a proxy, when proxies could be used, what domains of patient health they could report on, or how data should be reported. Observer-reported outcomes were typically defined as those based on observed behaviors, however there was not a consistent differentiation between proxy and observer reporting. Based on information extracted from these resources, we developed a checklist of considerations when including proxy-reported measures or using proxies in study design, data collection, analysis, interpretation and reporting of proxy reported data. CONCLUSION: Our targeted review highlights a lack of clarity in capturing, interpreting and reporting data from proxies in adult populations. We provide a checklist of considerations to assist researchers and clinicians with including proxies in research studies and clinical care. Lastly, our review identified areas where further guidance and future research are necessary.
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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 | 0.775 | 0.698 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.017 | 0.024 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".