Management of elderly patients with glioblastoma: current status with a focus on the post-operative radiation therapy
Bibliographic record
Abstract
Glioblastoma (GBM) is one of the most malignant primary intracranial neoplasms. This review aims to summarize the treatment of elderly patients with newly diagnosed GBM, with a focus on the radiation therapy (RT) approach. The available literature was reviewed, and we describe the most significant results relating to the post-operative approach of elderly GBM patients. Age limitations in randomized phase III studies have restricted the inclusion of elderly patients, and consequently, limited the generalizability of their results to this patient subset. Chronological age should not prohibit the best treatment, but instead, treatment decisions should consider patient functional status. RT showed efficacy and safety in the elderly population, without compromising quality of life. Hypofractionated RT is not inferior to standard RT. Reduction of overall RT schedule length mitigates the difficulties faced by elderly patients, improving treatment adherence. The addition of both concomitant and adjuvant temozolomide to standard RT is superior to either modality alone and should be the treatment of choice in the subset of patients with good/very good prognosis. It is reasonable to offer hypofractionated RT or temozolomide alone for poor prognosis, and best supportive care (BSC) for very poor prognosis elderly GBM patients. Although combined modality treatment is well established for the management of the good prognosis population, different RT schemes require further investigation with randomized controlled trials to determine the best regimen. A robust analysis of the molecular signatures of GBM in elderly patients might reveal opportunities for clinical protocol modifications to customize management in this group of patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".