Interpreting the Significance of Changes in Health-Related Quality-of-Life Scores
Bibliographic record
Abstract
PURPOSE: To determine the significance to patients of changes in health-related quality-of-life (HLQ) scores assessed by the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ-C30). PATIENTS AND METHODS: A subjective significance questionnaire (SSQ), which asks patients about perceived changes in physical, emotional, and social functioning and in global quality of life (global QL) and the QLQ-C30 were completed by patients who received chemotherapy for either breast cancer or small-cell lung cancer (SCLC). In the SSQ, patients rated their perception of change since the last time they completed the QLQ-C30 using a 7-category scale that ranged from "much worse" through "no change" to "much better." For each category of change in the SSQ, the corresponding differences were calculated in QLQ-C30 mean scores and effect sizes were determined. RESULTS: For patients who indicated "no change" in the SSQ, the mean change in scores in the corresponding QLQ-C30 domains was not significantly different from 0. For patients who indicated "a little" change either for better or for worse, the mean change in scores was about 5 to 10; for "moderate" change, about 10 to 20; and for "very much" change, greater than 20. Effect sizes increased in concordance with increasing changes in SSQ ratings and QLQ-C30 scores. CONCLUSION: The significance of changes in QLQ-C30 scores can be interpreted in terms of small, moderate, or large changes in quality of life as reported by patients in the SSQ. The magnitude of these changes also can be used to calculate the sample sizes required to detect a specified change in clinical trials.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".