Patients with Pretreatment Leukoencephalopathy and Older Patients have More Cognitive Decline after Whole Brain Radiotherapy
Bibliographic record
Abstract
Abstract Purpose: To investigate predictors of cognitive decline after whole brain radiotherapy (WBRT) for brain metastases. Methods: A secondary analysis of a phase 2 clinical trial was conducted in patients who received stereotactic radiosurgery for 1-10 brain metastases and WBRT (NCT01046123). The Montreal Cognitive Assessment (MoCA) was performed at baseline and every three months after WBRT. Baseline T2-weighted fluid attenuation inversion recovery magnetic resonance imaging was independently assessed by two neuroradiologists for the presence of white matter hyperintensities (WMH) using the Fazekas visual rating scale. WMH were also manually segmented for volumetric analysis. Univariable and multivariable logistic regression were used to test the association between baseline variables and MoCA score decline. Results: Forty-six patients survived ≥3 months after treatment. Age (OR 1.12 (1.04 – 1.21, p < 0.01), baseline WMH volume (OR 1.20, 95% CI 1.06 – 1.52, p = 0.02) and baseline Fazekas score ≥ 3/6 (OR 6.4, 95% CI 1.7 – 24.7, p < 0.01) were predictive of MoCA score decline. In multivariable analysis, age was the only significant predictor of MoCA decline. However, all three patients with pre-treatment leukoencephalopathy (Fazekas score = 6/6) had notable adverse outcomes due to cognitive impairment: one required full-time home nursing support and two were institutionalized.Conclusion: A greater decline in cognition after WBRT was observed in older patients and patients with a higher baseline WMH burden. Although this study is small and hypothesis-generating, we propose that radiation oncologists should exercise caution in prescribing WBRT if leukoencephalopathy is present on pre-treatment imaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".