Bibliographic record
Abstract
Limited healthcare dollars have resulted in insistence that the benefit of new therapies be evaluated before being approved for marketing or reimbursement under health service systems. Adequate evidence of a treatment's effectiveness includes evidence of impact on patient's health-related quality of life, including physical, mental, and emotional health. There are two types of measures of health-related quality of life. One, general health and utility measures, inquire about health in a broad sense, and can be applied and compared across many situations. The second type, specific measures, addresses narrower aspects of life related to a specific problem, function, or manifestations of an underlying disease process. Results of studies focusing on health-related quality of life only will be useful if the measurement instrument is valid and capable of detecting important change. Investigators should make a good choice of measurement instrument, and then ensure their study design will yield valid results. We offer basic guidelines for the measurement of health-related quality of life as an outcome in clinical research. This discussion addresses clinicians, who are making decisions regarding the relevance of study results, and investigators who are designing studies.
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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.048 |
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".