Neurocognitive outcome of HS-WBRT vs WBRT in patients with brain metastases: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Whole-brain radiation therapy (WBRT) is used prophylactically and therapeutically in patients with brain metastases, effectively controlling intracerebral tumors and reducing neurological mortality. However, WBRT poses a significant risk of cognitive decline. Hippocampus-sparing WBRT (HS-WBRT) offers a potential solution by preserving memory and other cognitive functions. This study evaluates neurocognitive outcomes of HS-WBRT compared to WBRT in patients with brain metastases. Methods A systematic search was conducted in MEDLINE, Google Scholar, Embase, and CENTRAL for cohort studies and clinical trials reporting neurocognitive outcomes of HS-WBRT vs WBRT, up to March 2024. Non-English studies and those lacking neurocognitive outcomes were excluded. Eligible studies underwent data extraction and analysis focused on neurocognitive function testing. Results Of 9 eligible studies, 7 were included in the quantitative analysis. HS-WBRT significantly reduced cognitive decline compared to WBRT, with improvements in Hopkins Verbal Learning Test (HVLT) scores for total recall (SMD = 0.42; P = .02) and delayed recall (SMD = 0.25; P = .02). Cognitive impairment measured by the Montreal Cognitive Assessment (MoCA) was also significantly lower in the HS-WBRT group (SMD = 1.21; P < .00001). Conclusion HS-WBRT demonstrates a clear advantage over WBRT in preserving neurocognitive function in patients with brain metastases, as reflected in HVLT and MoCA scores. Future studies should further explore adverse effects and survival outcomes to guide clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".