The Brain Metastases Symptom Checklist as a Novel Tool for Symptom Measurement in Patients with Brain Metastases Undergoing Whole-Brain Radiotherapy
Bibliographic record
Abstract
PURPOSE: We evaluated the feasibility, reliability, and validity of the Brain Metastases Symptom Checklist (bmsc), a novel self-report measure of common symptoms experienced by patients with brain metastases. METHODS: Patients with first-presentation symptomatic brain metastases (n = 137) referred for whole-brain radiotherapy (wbrt) completed the bmsc at time points before and after treatment. Their caregivers (n = 48) provided proxy ratings twice on the day of consultation to assess reliability, and at week 4 after wbrt to assess responsiveness to change. Correlations with 4 other validated assessment tools were evaluated. RESULTS: The symptoms reported on the bmsc were largely mild to moderate, with tiredness (71%) and difficulties with balance (61%) reported most commonly at baseline. Test-retest reliability for individual symptoms had a median intraclass correlation of 0.59 (range: 0.23-0.85). Caregiver proxy and patient responses had a median intraclass correlation of 0.52. Correlation of absolute scores on the bmsc and other symptom assessment tools was low, but consistency in the direction of symptom change was observed. At week 4, change in symptoms was variable, with improvements in weight gain and sleep of 42% and 41% respectively, and worsening of tiredness and drowsiness of 62% and 59% respectively. CONCLUSIONS: The bmsc captures a wide range of symptoms experienced by patients with brain metastases, and it is sensitive to change. It demonstrated adequate test-retest reliability and face validity in terms of its responsiveness to change. Future research is needed to determine whether modifications to the bmsc itself or correlation with more symptom-specific measures will enhance validity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".