Interpreting Within-Patient Changes on the EORTC QLQ-C30 and EORTC QLQ-LC13
Bibliographic record
Abstract
INTRODUCTION: When determining if changes on patient-reported outcome (PRO) scores in clinical trials convey a meaningful treatment benefit, statistical significance tests alone may not communicate the patient perspective. Appraising within-patient changes on PRO scores against established thresholds can determine if improvements or deteriorations experienced by individuals are meaningful. To evaluate the appropriateness of thresholds for interpreting meaningful improvements and deterioration within individuals on the European Organisation for Research and Treatment of Cancer (EORTC) 30-item core instrument (QLQ-C30) and 13-item lung cancer module (QLQ-LC13), a series of psychometric methods were applied to data from a phase III randomized controlled clinical trial in non-small cell lung cancer. METHODS: Anchor-based methods of empirical cumulative distribution functions and classification statistics were employed using change scores from Baseline to Week 7 using changes on the QLQ-C30 Global Health Status item as an anchor. Distribution-based methods of one-half standard deviation and standard error of measurement identified the minimum amount of change each domain score can reliably measure. RESULTS: While the correlations between the domain scores and the anchor item were modest in size (i.e., r ≥ 0.30 for only 5 of 24 domains), consideration of multiple methods along with the magnitude of possible step changes on the score allowed for patterns to emerge. The triangulation process planned a priori resulted in different methods being the source for different domain scores. Absolute values of the proposed thresholds ranged from 11.11 to 33.33, and all resulted in the same classifications for all EORTC domains, except QLQ-C30 Fatigue, as would the 10-point threshold that is traditionally used. CONCLUSION: This study confirms the appropriateness of the 10-point EORTC score threshold generally used by the field for interpreting within-patient changes, but the thresholds proposed from this study enhance interpretability by corresponding to only observable locations along the domain score scale.
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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 | 0.076 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".