The EORTC QLQ-BN20 for assessment of quality of life in patients receiving treatment or prophylaxis for brain metastases: a literature review
Bibliographic record
Abstract
INTRODUCTION: Brain metastases occur in approximately 20-40% of cancer patients during the course of disease. As treatment for brain metastases is palliative over curative, quality of life (QoL) is emphasized over prolonged survival. The European Organization for Research and Treatment of Cancer (EORTC) QLQ-BN20 is a QoL assessment specific to brain neoplasms. We aim to provide a review of the current use of the EORTC QLQ-BN20 for patients with brain metastases. MATERIALS & METHODS: All studies utilizing the QLQ-BN20 for QoL assessment in patients receiving treatments related to brain metastases were included. Study information including treatment type, assessment periods, patient enrolment and all information pertaining to the QLQ-BN20 were extracted. RESULTS: A total of 13 studies were identified, five of which were randomized trials assessing prophylactic whole brain radiation for patients with small-cell lung cancer. The QLQ-BN20 was used in conjunction with the core QLQ-C30 questionnaire in all but one of the studies and together these comprised the entire QoL assessments for 11 of the 13 studies. Neurocognitive function assessments supplemented QoL in four studies and accompanying performance status indices used with the QLQ-BN20 varied. Compliance issues were commonly cited. QoL changes during study periods varied as improvements, deteriorations and stabilizations were all observed. CONCLUSION: QoL assessments should be conducted using disease-specific tools. Future studies should minimize patient burden in order to maximize data collection and accrual. A common set of QoL end points for patients with brain metastases should be created.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".