Assessment by proxy of the SF-36 and WHO-DAS 2.0. A systematic review
Bibliographic record
Abstract
Background and objective: In some cases, for the evaluation of the health status of patients it is not possible to obtain data directly from the patient. The objective of this study was to determine if the instruments that cannot be applied to the patient can be completed by a proxy.Methods: A systematic review of the literature was carried out and 20 studies were included. The instruments reviewed in this synthesis were: Short Form-36 (SF-36), Montreal Cognitive Assessment (MoCA), WHODAS 2.0, Patient Health Questionnaire 9 (PHQ-9), State-Trait Anxiety Inventory (STAI), Disability Rating Scale (DRS).Results: The levels of agreement between the responses of the patients and the proxies were good, mainly when evaluating HRQoL and functioning with the SF-36 and WHODAS 2.0 instruments, respectively, with a higher level of agreement in the more objective and observable domains such as physical functioning and lower level of agreement in less objective domains, such as emotional or affective status, and self-perception.Conclusion: In patients who cannot complete the different instruments, the use of a proxy can help avoid the omission of responses. LAY ABSTRACTPeople with certain mental or neurological illnesses are often unable to answer questions about their health sta-tus, functional ability, or quality of life. In some cases, a relative or a person who knows the patient can fill out questionnaires to find out how affected he/she is, detect changes in his/her condition and even evaluate the response to the interventions performed. These people are known as proxies. This research sought to assess which questionnaires for measuring depression, anxiety, neurocognitive impairment, quality of life, function, or disability can be answered by a proxy, when patients cannot answer for themselves. For this, the medical literature published on this subject was reviewed. Twenty studies showing a good agreement between the responses of the patients and the proxies were found, especially in the assessment of quality of life and functional capacity. The use of a proxy can help avoid the omission of responses.
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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 | 0.020 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".