Bibliographic record
Abstract
One of the reasons behind the rapid development of quality of life measures in health care has been the growing recognition of the importance of understanding the impact of healthcare interventions on patients' lives rather than just on their bodies. This is particularly important for patients with chronic, disabling, or life threatening diseases who live without the expectation of cure and have conditions that are likely to have an impact on their physical, psychological, and social wellbeing. Health professionals frequently make quality of life judgments when making decisions about the care of disabled patients,1 and the professional's view on expected quality of life is often the key factor in determining whether effective treatment for a life threatening condition will be given or withdrawn. Professionals' perceptions may, however, be at odds with those held by their patients. It is therefore important to ask patients to assess their own quality of life using one of a growing number of reliable and valid measures. Choosing an appropriate measure and using it in clinical practice can be problematic. Deciding to use a measure, however, presupposes that patients are able to assess their own quality of life and complete a quality of life measure. Some patients—and in some conditions many patients—are unable to do this because of cognitive impairments, communication deficits, severe distress caused by their symptoms, or because the quality of life measure is too burdensome physically or emotionally. These may be precisely the patients for whom information on quality of life is most needed to inform clinical decision making. Rather than lose all information on that patient, someone else (a family member or health professional) may be asked to act as a proxy or surrogate. In this paper the use of proxies to measure quality of life is addressed. We consider the advantages and disadvantages of using proxies to rate quality of life, debate the reasons why a proxy's view and a patient's view may differ, and suggest directions for future research.
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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 | 0.011 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".