The EORTC QLQ breast modules and the FACT-B for assessing quality of life in breast cancer patients – an updated literature review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Two commonly used quality of life questionnaires in breast cancer are EORTC QLQ-BR23, the FACT-B, and the extended FACT-B + 4. More recently, the EORTC EORTC QLQ-BR42 was developed. This systematic review compares the various versions of the EORTC QLQ and FACT tools for breast cancer in terms of their content, validity, and psychometric properties. RECENT FINDINGS: Thirty-six studies met the inclusion criteria. All questionnaires have been proven to be valid, reliable and responsive. The provisional EORTC QLQ-BR45 transitioned to the EORTC QLQ-BR42 in Phase IV of its development, which encompasses the side effects associated with the latest breast cancer treatments. Both the EORTC and FACT measures assess physical and mental dimensions of quality of life, with the EORTC measure placing relatively more emphasis on physical content and FACT placing relatively more emphasis on mental (social and emotional) content. The four additional items in the FACT-B + 4 were developed to address arm lymphoedema following axillary surgery. SUMMARY: The development and uptake of quality of life tools are essential in the evaluation of breast cancer treatments. The EORTC QLQ-BR42 and FACT-B are both valid, reliable, and responsive QoL questionnaires.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".