Health-Related Quality of Life Measurement in Symptom Management Trials
Bibliographic record
Abstract
There is increasing support for the incorporation of patient-reported outcomes (PROs) into clinical trials in cancer. While the need for inclusion of measures of target symptoms in symptom management trials is clear, arguments can also be made for measurement of a broader range of symptoms, for evaluation of symptom burden, and for evaluation of health-related quality of life (HRQOL) in these trials. What is key to their inclusion is a priori selection of instruments, provision of a theoretic basis for inclusion of instruments, and a clearly described plan of analysis. The federal Food and Drug Administration (FDA) has provided guidance regarding the use of PROs (symptom and HRQOL measures) to support treatment benefit claims in product labeling. Moving forward, research is needed to address methodological issues raised by the FDA and to increase understanding of relationships among symptoms, symptom clusters, HRQOL, and other outcome measures.
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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 | 0.151 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".