A review of proton beam therapy’s role in glioma management
Bibliographic record
Abstract
Gliomas pose significant therapeutic challenges due to limited survival and risks of treatment-related toxicity. Proton beam therapy (PBT) offers a precise radiation delivery method, minimizing damage to healthy brain tissue compared to conventional radiotherapy. This review synthesizes findings from 15 studies (2000-January 2024) from PubMed, Google Scholar, Science Direct, Cochrane Library, and Directory of Open Access Journals. PBT significantly reduces neurocognitive decline and enhances quality of life while achieving comparable or superior survival outcomes across various glioma types, including low-grade gliomas and glioblastomas. Notable benefits include improved verbal memory, stable intellectual functioning, and reduced high-grade toxicity. However, challenges such as neuroendocrine deficiencies and increased radiation necrosis highlight the need for optimized protocols. Future research should focus on comprehensive outcome assessments, proactive management of adverse effects, and improving PBT accessibility through cost reduction and technological advancements. PBT holds transformative potential in glioma management, balancing effective tumor control with preservation of neurological function, positioning it as a valuable neuro-oncology treatment option.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".