SURG-33. GLIOBLASTOMA IN THE ELDERLY: THE IMPACT OF EXTENT OF RESECTION ON SURVIVAL
Bibliographic record
Abstract
Glioblastoma (GBM) occurs commonly in individuals above 65 years of age. Most clinical trials that guide recommendations for treatment of GBM tend to exclude patients above the age of 65 years. In this study, we evaluated a cohort of elderly patients treated at a single institution in order to identify factors that impact overall survival. A retrospective review was performed of elderly patients (≥ 65 years old) with newly diagnosed GBM treated between 2013 and 2016. Tumor volumetric analysis was performed using the Osirix software. Various characteristics were evaluated in univariate and multivariate stepwise models to examine their effects on overall survival. 63 patients were included in the study with a median age of 72 years. Tumors most frequently involved the temporal lobe (36.5%), followed by the frontal lobe (34.9 %), parietal (30.1%), and occipital (6.3%) lobes. Pre-operative tumor volume was 30.6 cm3. Four patients (6.3%) underwent biopsy only and the remaining 93.7% of patients underwent resection with many (55%) having gross-total resection (GTR). The majority (95.2%) of the patients received postoperative radiotherapy or radiotherapy and concurrent temozolomide. The mean survival for all patients was 12 months; three patients experienced long-term (≥ 2-year) survival. Extent of resection was seen to significantly impact overall survival; patients who underwent GTR had a median survival of 15.8 months, whereas those who underwent subtotal resection had survival of 9.8 months (p<0.05). This study demonstrates that extent of resection positively influences overall survival in elderly patients with GBM, suggesting that safe maximal resection should be considered. Furthermore, administration of the postoperative chemoradiotherapy can improve survival and quality of life for this elderly patient population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".