NCOG-17. PREDICTORS OF SURVIVAL IN ELDERLY PATIENTS UNDERGOING SURGERY FOR GBM
Bibliographic record
Abstract
Abstract BACKGROUND Despite the median age of diagnosis of GBM being 64 years old, there is only a paucity of studies on elderly patients with GBM. Furthermore, the majority of these studies examine treatment paradigms, and there is limited research on clinical and hospital factors on overall survival. The purpose of this study is to determine predictors of survival in elderly patients undergoing surgery for GBM. METHODS We searched our hospital brain tumour biobank database for all consecutive patients over a 14-year period from 2005 to 2018. All patients 65 years of age or older at time of surgery with a pathological diagnosis of de novo primary GBM were included. Kaplan-Meier survival curve and Cox proportional hazards model were constructed for overall survival vs age, sex, KPS, medical co-morbidities, extent of resection by surgeon, length of stay, postop complications, and discharge destination. RESULTS A total of 150 patients were included. The median age at time of surgery was 74 years old (range: 65-94). Median overall survival was 9.4 months (95% CI: 7.8-12.2). Variables associated with worse survival included longer length of stay (HR: 1.15, 95% CI: 1.02-1.30, p = 0.02), discharge destination other than home (HR: 1.91, 95% CI: 1.01-3.6, p = 0.04), and any postop complication (HR: 3.7, 95% CI: 1.87-7.3, p = < 0.001). The presence of any or multiple medical comorbidities was not associated with worse survival (p = 0.93 and 0.19, respectively). CONCLUSIONS The presence of medical comorbidities is not associated with worse survival in elderly patients undergoing surgery for GBM. In order to maximize survival in these patients, avoidance of postoperative complications is paramount, along with a short hospital stay and attempt to discharge these patients to their home.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".