Do General Dimensions of Quality of Life Add Clinical Value to Symptom Data?
Bibliographic record
Abstract
Since global health-related quality of life (GHRQL) reflects broad impacts of treatment, its assessment in an advanced-stage disease trial should add valuable clinical information beyond that of a targeted symptom. Using latent trajectory modeling that allowed for individual trends as well as overall relationships, we reanalyzed three repeated assessments of the present pain intensity from the McGill Pain Questionnaire and the European Organization for Research and Treatment of Cancer Quality of life Questionnaire- Core 30 (QLQ-C30) GHRQL score from a hormone-refractory prostate cancer trial. Within- and between-treatment differences not detected in the original S9916 report of pain palliation and GHRQL suggested substantial individual variation in GHRQL level and change after controlling for within-assessment pain. The treatment had a differential effect on the relationship between GHRQL and pain; we observed an approximately threefold stronger association of reported pain with GHRQL in the docetaxel plus estramustine (D + E) arm compared with the mitoxantrone plus prednisone (M + P) arm (P = .02). In addition, the treatment had an effect, on average, on the rate of change in GHRQL, after controlling for pain level. GHRQL for patients on the M + P arm tended to improve over the assessment period while GHRQL tended to deteriorate for patients on the D + E arm (P = .02). Important, interpretable effects and systematic individual variation in GHRQL remain after controlling statistically for the effects of pain, the targeted symptom, in this trial. In addition, identifying the rate at which a person's GHRQL changes or responds to treatment provides clinically relevant information.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".