Management and Outcomes in the Oldest-Old Population with Glioblastoma
Bibliographic record
Abstract
OBJECTIVES: Glioblastoma is a lethal disease in the elderly population. We aimed to evaluate disease and treatment outcomes in the oldest-old patients. METHODS: Patients >80 years old with histologically confirmed glioblastoma treated between 2004 and 2009 were identified. We included patients managed with best supportive care (BSC), temozolomide (TMZ) alone, radiotherapy (RT) alone, or concomitantly with TMZ (CRT). Survival outcomes were analyzed using the Kaplan-Meier method. RESULTS: Ultimately, 48 patients were analyzed. Median age and Eastern Cooperative Oncology Group (ECOG) Performance Status were 82 years and 2, respectively. The median Age-Adjusted Charlson Index (AAC) was 6. Gross total and subtotal resections were performed in 16.7% and 18.8% of patients, respectively. Biopsy followed by RT alone was the treatment modality for 23/48 (47.9%), while 17/48 (35.4%) received surgery followed by RT alone or CRT. A total of 8 (16.7%) were managed with BSC after biopsy. Median overall survival (OS) and progression-free survival (PFS) were 4.1 (95% confidence interval [95% CI] 3.3-4.9) and 2.7 (95% CI 1.5-3.9) months, respectively. Improved median OS was observed in those treated with surgical resection followed by RT alone or CRT (7.1 months), compared to biopsy followed by RT alone (4.2 months) or BSC (2.0 months; p=0.002). Surgical resection, age≤85, and AAC<6 were associated with better OS (p=0.032, p=0.031, and p=0.02, respectively). Cause of death was neurological progression in 56% of cases. RT was well-tolerated. CONCLUSIONS: PFS and OS outcomes remain poor in the oldest-old patients (>80 years old). Younger age, lower AAC, surgical resection, and adjuvant treatment were associated with improved OS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".