Surgical Resection Predicts Overall Survival even in Frail Patients with IDH- wildtype Glioblastomas
Bibliographic record
Abstract
Abstract Purpose The aim of this study was to evaluate the role of frailty required in the surgical risk assessment in old patients with IDH-wildtype glioblastoma on surgical outcome, overall survival, and functional dependency. Methods We reviewed records of old and frail patients surgical treated at our institution between January 2018 to May 2021. Inclusion criteria were : 1) neuropathological diagnosis of IDH-wildtype glioblastoma; 2) patient older than 65 years at the time of surgery; 3) available data to assess the frailty index according to the 5-mFI. Results A total of 47 patients were included. The 5-mFI was at 0 in 11 cases (23.4%), at 1 in 30 cases (63.83%), at 2 in 2 cases (4.25%), at 3 in 2 cases (4.25%), at 4 in 2 cases (4.25%). A GTR was performed in 26 patients (55.3%), a STR was performed in 13 patients (27.6%), and a B was performed in 8 patients (17.1%). The rate of 30-day postoperative complications was higher in B and in the 5-mFI = 4 subgroup (p > 0.05). GTR and age ≤ 70 years were independent predictors of a longer overall survival (p < 0.005). Sex, 5-mFI, postoperative complications, and preoperative KPS status did not independently influence overall survival and functional dependency. Conclusion In aged patients with IDH-wildtype glioblastoma, GTR is still an independent predictor of longer survival and good postoperative functional recovery whatever the frailty index. The 5-mFI score does not influence surgery and outcomes. Further confirmatory analyses are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".