EORTC QLQ-BR23 and FACT-B for the assessment of quality of life in patients with breast cancer: a literature review
Bibliographic record
Abstract
BACKGROUND: This study aims to compare the development, characteristics and validity of two widely used tools in the breast cancer population, the EORTC QLQ-BR23 and the FACT-B. METHODS: A literature search was conducted using Ovid MEDLINE, OLDMEDLINE, Embase, Embase Classic and the Cochrane Central Register of Controlled Trials to identify relevant studies. RESULTS: Both tools were found to be reliable and valid. The QLQ-BR23 focuses on physical function, whereas the FACT-B emphasizes emotional well-being. Scoring, item format, organization and response options differ between questionnaires. CONCLUSION: Overall, both questionnaires are effective in assessing breast cancer-specific quality of life. Clear similarities and differences between the two tools exist. Decision-making between the questionnaires should be based on the purpose and design of the study.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".