Predictors of survival in elderly patients undergoing surgery for glioblastoma
Bibliographic record
Abstract
Abstract Background Glioblastoma (GBM) has a median age of diagnosis of 64 years old and the incidence increases with age. An increasing number of elderly patients are being diagnosed with GBM and undergoing surgery. These patients often present with multiple medical comorbidities and have significantly worse outcomes compared to adult patients. The goal of this study was to determine clinical predictors of survival in elderly patients undergoing surgery for GBM. Methods Our brain tumor database was reviewed for all patients 65 years of age and older that underwent surgery for newly diagnosed GBM over a 14-year period from 2005 to 2018. Patient characteristics, comorbidities, complications, and treatment were collected. A total of 150 patients were included, and subdivided into two age categories; 65–74 years old and 75 years or older. Results The median OS for all patients was 9.4 months. Neither the presence nor number of medical comorbidities were associated with decreased survival (P = .9 and P = .1, respectively). Postoperative complications were associated with worse survival for all patients (HR = 2.34, P = .01) and occurred in patients in the older age category and patients with longer lengths of stay (P < .0001). Conclusions The presence of medical comorbidities is not a reason to exclude patients with GBM from surgical consideration. Excluding EOR and adjuvant treatment, postoperative complication is the most significant predictor of survival in elderly patients. Postoperative complications are associated with a longer LOS and are more common in patients 75 years of age and older.
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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 | 0.001 | 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) | 0.001 | 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".