Prognostic factors and survival of recurrent glioblastoma: a systematic review
Bibliographic record
Abstract
Introduction : Glioblastoma is a highly aggressive brain cancer with poor prognosis. Recurrence is common, and survival post-recurrence is limited. Identifying prognostic factors for recurrent glioblastoma can optimize treatment and improve outcomes. Aim : This systematic review analyzed the clinical, molecular, and treatment-related variables that influence survival in patients with recurrent glioblastoma. Materials and methods : A comprehensive search of PubMed, Scopus, and ProQuest databases included studies from the past decade, assessed using the Newcastle-Ottawa Scale (NOS). Results : Sixteen studies were analyzed, highlighting age, Karnofsky Performance Status (KPS), molecular markers (MGMT promoter methylation, IDH mutations, TERT promoter mutations, TP53 alterations, ATRX loss, and Ki-67 expression), and surgical resection extent as key prognostic factors. Younger patients with higher KPS scores and favorable molecular markers had better survival. Molecular profiling and maximal resection correlated with improved overall survival (OS). Salvage therapies like chemotherapy and re-resection provided marginal benefits, with variability based on patient demographics and tumor genetics. Conclusion : Age, KPS, molecular markers, and surgical resection extent significantly predict survival in recurrent glioblastoma. The review underscores the importance of molecular profiling for personalized treatment, though current salvage therapies show limited effectiveness. Innovative approaches are needed to enhance outcomes for this aggressive disease. Abbreviations used in the article: BSC : best supportive care; CRE : complete resection of enhancing tumor; DFS : disease-free survival; GBM : glioblastoma; GTR : gross total resection; KPS : Karnofsky Performance Status; NOS : Newcastle-Ottawa Scale; OS : overall survival; PFS : progression-free survival; rGBM : recurrent glioblastoma multiforme; RTOG–RPA : Radiation Therapy Oncology Group – Recursive Partitioning Analysis; TTF : tumor-treating fields
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".