Health-related quality of life in elderly: a review of the EQ-5D use
Bibliographic record
Abstract
Objective: To systematically identify and review studies that used EQ-5D to assess health-related quality of life (QoL) in elderly. Methods: Relevant literature was searched in MEDLINE and Lilacs databases and the EuroQol Plenary Meetings Proceedings (June/2003 to June/2013). The inclusion criteria were subjects aged 60 years or more and the use of the EQ-5D questionnaire. Two independent reviewers screened title, abstract, full text and performed data extraction. The country where the study had been conducted, demographic characteristics of the population, objectives, common criteria used by the studies to the exclusion of patients/participants and presentation of the data were the variables analyzed. Results: A total of 90 studies were included with 34,449 subjects, the mean age was 75.6 ± 4.3 years. The majority of the studies were from Europe (66.7%). Studies in Africa and South America were not identified. The main diseases investigated were orthopedic (20.0%) and cardiovascular diseases (15.5%). The study’s results were most frequently based on personal interviews (41.1%) involving directly the elderly (92.2%). The most common exclusion criteria were health conditions that could result in bias or confounding on the study protocol (61.1%) and low cognitive level (50.0%). The EQ-5D results were presented in different ways: means (82.2%) or me[1]dians (5.6%) associated with measures of dispersion as standard deviation (61.1%) and confidence interval (22.2%), or according to the answers in the descriptive system (22.2%). Conclusions: The lack of standardization in the exhibition of the results limits a direct comparison among different interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| 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".