Economic evaluation and health-related quality of life
Bibliographic record
Abstract
Health-related quality of life (HRQL) is concerned with the opportunities that a person's health status affords, the constraints that it places upon the person and the value that a person places on his or her health status. The rationale for measuring HRQL falls into three categories: discrimination, evaluation, and prediction. Measures have to meet generally accepted psychometric criteria such as acceptability, reliability/reproducibility, responsiveness, validity, interpretability, and usefulness. HRQL instruments have been designed for self-administration or administration by interviews and some have been adapted to multiple cultural/linguistic needs. For adolescents and young adults with cancer several instruments are available. Overall HRQL is compromised, to varying degrees, in such survivors by comparison with peers in the general population; and the burden of morbidity is greatest after brain and bone tumors. As there is a burden of treatment-related morbidity and as the number of survivors within the health care system is growing, the economic dimension of care and cure has to be taken into consideration. Economic evaluation affords a comparison of the costs and consequences (effects) of relevant therapeutic alternatives. The future research activities with respect to HRQL have to consider these new dimensions of care.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".