Canadian Expert Consensus Recommendations for the Diagnosis and Management of Glioblastoma: Results of a Delphi Study
Bibliographic record
Abstract
Glioblastoma is the most common and aggressive malignant brain tumor in adults, with an increasing incidence and a poor prognosis. Current challenges in glioblastoma management include rapid tumor growth, limited treatment effectiveness, high recurrence rates, and a significant impact on patients' quality of life. Given the complexity of glioblastoma care and recent advancements in diagnostic and treatment modalities, updated guidelines are needed in Canada. This Delphi study aimed to develop Canadian consensus recommendations for the diagnosis, classification, and management of newly diagnosed and recurrent glioblastoma. A multidisciplinary panel of 14 Canadian experts in glioblastoma care was convened, and a comprehensive literature review was conducted to synthesize evidence and formulate initial recommendations. Consensus was achieved through three Delphi rounds, in which panelists rated their agreement with recommendation statements on a five-point Likert scale. Statements with ≥75% agreement were accepted, and others were revised for re-voting. Final recommendations were formulated based on the consensus level, strength of evidence, clinical expertise, and consideration of the Canadian healthcare context. These recommendations aim to standardize glioblastoma diagnosis and classification across Canada, provide evidence-based guidance for optimal treatment selection, integrate novel therapies, and enhance the overall quality of care for glioblastoma patients.
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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 | 0.230 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".