Narrative review of neurocognitive and quality of life tools used in brain metastases trials
Bibliographic record
Abstract
Development of brain metastases are common in patients with advanced malignancies leading to significant morbidity and mortality. Although overall survival is an important endpoint in these patients, neurocognition and health related quality of life (HRQoL) more accurately highlights the impact of the disease and its treatment on patients. Whole brain radiotherapy (WBRT) has historically played a key role in the management of these patients, especially those with multiple brain metastases. Clinical trials have supported the use of stereotactic radiosurgery (SRS) alone in patients with limited brain metastases sparing neurocognitive function and HRQoL as compared to the combination of SRS plus WBRT. Furthermore, new systemic agents are increasingly being used in clinical practice and have shown promise in patients with brain metastases. The upcoming clinical trials are tasked with defining treatment guidelines that are more specific to patient and tumour factors incorporating radiation, surgery, and systemic therapy. The validity of findings in these trials rest on the rigor of the study methodology and the utilisation of validated assessment tools for neurocognition and HRQoL. This review aims to appraise and summarise the neurocognitive and HRQoL tools used in modern brain metastases trials.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| 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".