FACT-Br for assessment of quality of life in patients receiving treatment for brain metastases: a literature review
Bibliographic record
Abstract
INTRODUCTION: Brain metastases are a significant cause of morbidity and mortality for patients with advanced cancers, and quality-of-life (QoL) end points are most appropriate for this population. The Functional Assessment of Cancer Therapy (FACT) questionnaires are commonly used to assess cancer-related QoL issues. The FACT-Brain (FACT-Br) provides an additional set of disease-specific questions pertaining to brain neoplasms. We aim to provide a comprehensive review to examine the use of the FACT-Br as a QoL assessment for patients with brain metastases. MATERIALS & METHODS: A review of the literature was conducted and all studies utilizing the FACT-Br for QoL assessment of patients with brain metastases were included. Study information and relevant information regarding the FACT-Br were extracted. RESULTS: A total of 14 studies were identified representing various treatment options (radiation, chemotherapy and surgery) for patients with brain metastases. All studies utilized at least part of the FACT-Br as the main QoL assessment. In addition, neurocognitive and performance status assessments were performed in nine and 12 out of 14 studies, respectively. Issues of poor accrual, compliance and attrition were common and posed problems in reaching statistically significant changes in QoL despite changes in raw QoL scores. CONCLUSION: Studies involving patients with brain metastases should continue to utilize QoL tools such as the FACT-Br; however, this tool still requires validation for use in this patient population. Additional studies should observe the relationship between neurocognitive function and QoL, and attempt to minimize poor accrual and compliance issues through modifications of trial design and reduction of patient burden.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| 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) | 0.004 | 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".