Validation of the cancer-specific utility measure EORTC QLU-C10D using evidence from four lung cancer trials covering six country value sets
Bibliographic record
Abstract
The Quality of Life (QoL) Utility measure, QLU-C10D, is derived of the European Organisation for Research and Treatment of Cancer (EORTC) QoL Questionnaire, QLQ-C30. Based on the cancer-specific nature, the QLU-C10D is expected to be sensitive and responsive in lung cancer patients.This retrospective analysis used data from four international lung cancer multi-center trials (NCT00656136, NCT00949650, NCT01085136, NCT01523587). Clinical validity was assessed in comparison to a generic standard utility instrument, the EuroQoL Group´s EQ-5D-3L. Utilities of six country value sets (Australia, Canada, Italy, the Netherlands, Poland, UK) were calculated at baseline and end of treatment for both measures. Country value set pairs of both measures (k) were compared in terms of Relative Efficiency (RE) and difference in Effect Sizes (dES) in 1) sensitivity to detect differences between performance status groups and 2) responsiveness to changes at each trial sample. Analysis of the four trials (N1 = 496, N2 = 290, N3 = 202, N4 = 770) with the six country value sets of each utility measure showed ad 1) Sensitivity indices favored the QLU-C10D (k = 18, p ≤ 0.019; RE > 1.10; dES > 0.03), and ad 2) Responsiveness indices of changes within clinically known groups (k = 78), largely favored QLU-C10D (k = 74, p ≤ .024; RE > 1.01; dES > 0.02), in comparison with the generic utility instrument. In summary, 96% of the comparative indices favored the QLU-C10D. In summary, this study confirms the clinical validity of the QLU-C10D in lung cancer patients. The QLU-C10D produced homogenous results across six country value sets and detected differences/changes in alignment with clinical expectations. In most comparisons the QLU-C10D was more sensitive or responsive compared to the EQ-5D-3L.
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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 | 0.305 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".