Revisiting the use of the EORTC QLQ-STO22 to assess health-related quality of life of patients with gastric cancer: incorporating updated treatment options and cross-cultural perspectives
Bibliographic record
Abstract
BACKGROUND: The EORTC QLQ-STO22 (QLQ-STO22) is a firmly established and validated measure of health-related quality of life (HRQoL) for people with gastric cancer (GC), developed over two decades ago. Since then there have been dramatic changes in treatment options for GC. Also, East Asian patients were not involved in the development of QLQ-STO22, where GC is most prevalent and the QLQ-STO22 is widely used. A review with appropriate updating of the measure was planned. This study aims to capture HRQoL issues associated with new treatments and the perspectives of patients and health care professionals (HCPs) from different cultural backgrounds, including East Asia. METHODS: A systematic literature review and open-ended interviews were preformed to identify potential new HRQoL issues relating to GC. This was followed by structured interviews where HCPs and patients reviewed the QLQ-STO22 alongside new issues regarding relevance, importance, and acceptability. RESULTS: The review of 267 publications and interviews with 104 patients and 18 HCPs (48 and 9 from East Asia, respectively) generated a list of 58 new issues. Three of these relating to eating small amounts, flatulence, and neuropathy were recommended for inclusion in an updated version of the QLQ-STO22 and covered by five additional questions. CONCLUSIONS: This study supports the content validity of the QLQ-STO22, suggesting its continued relevance to patients with GC, including those from East Asia. The updated version with additional questions and linguistic changes will enhance its specificity, but further testing is required.
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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 | 0.118 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".