Comparing FACT and EORTC QLQ modules for the assessment of quality of life in patients with hepatobiliary cancers
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Four commonly used quality of life (QoL) questionnaires for patients with hepatobiliary cancers are the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Liver Module (QLQ-LMC21), the Quality of Life Questionnaire Hepatocellular Carcinoma-Specific Module (QLQ-HCC18), the Quality of Life Questionnaire Biliary Tract Cancer and Gallbladder Cancer Module (QLQ-BIL21), and the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep). The objective of this systematic review is to compare the characteristics and psychometric properties of these four QoL instruments. RECENT FINDINGS: From 276 studies, 14 were included: QLQ-LMC21 (3), QLQ-HCC18 (6), QLQ-BIL21 (2), and FACT-Hep (3). All were rigorously developed using a multiphase, standardised approach and shown to be psychometrically valid. In the development/validation of the QLQ-LMC21 and QLQ-BIL21, a majority of patients were recruited from European countries, but race was not specified. In contrast, the QLQ-HCC18, despite including a greater proportion of East Asian participants, lacked representation from other regions and races. Furthermore, challenges in assessing jaundice in Asian patients were identified during the validation phase. The FACT-Hep was developed in the United States and only validated in the United States (90% Caucasian) and China. Notably, QLQ-BIL21 was limited by its small sample size ( n = 52) during the Phase III of its development. SUMMARY: The EORTC QLQ-LMC21, QLQ-HCC18, QLQ-BIL21, and FACT-Hep have proven to be reliable, valid, and responsive. However, additional cross-cultural validation studies may enhance global applicability.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".