Use of the EORTC QLQ-BN20 and the FACT-Br for the assessment of quality of life in patients with brain tumors: a systematic review of prospective clinical studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: This systematic review aims to evaluate how the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Brain Cancer (EORTC QLQ-BN20) and Functional Assessment of Cancer Therapy-Brain (FACT-Br) are used in prospective brain tumor studies in the past decade, particularly in assessing quality of life (QoL). It aims to assess variability in QoL outcomes across treatment types, use of supplemental tools, and assessment of data completeness and concordance with cognitive assessments. RECENT FINDINGS: A total of 100 prospective studies were included and reviewed. The EORTC QLQ-BN20 was used in 75 studies, and the FACT-Br in 27; 2 studies used both. Patient-reported outcome measures were supplemented in 98 studies, most commonly with the EORTC QLQ-C30, EQ-5D, or FACT-G. Fifteen studies included neurocognitive assessments. QoL was the primary endpoint in 39 studies. Radiotherapy and systemic therapy were the most frequently studied interventions (36 studies each), followed by surgical interventions (34 studies). QoL outcomes varied by intervention type. Seven of 15 studies using cognitive testing reported discordance between objective and self-reported cognition. Thirty-four studies reported compliance challenges, and 19 reported ≥25% missing data at final follow-up. SUMMARY: The QLQ-BN20 and FACT-Br are widely used tools for QoL evaluation in brain tumor research. Enhancing their usability, incorporating digital formats, and integrating cognitive testing may improve data quality and relevance in clinical practice.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".