Evolution of the Neurosurgeon's Role in Clinical Trials for Glioblastoma: A Systematic Overview of the Clinicaltrials.Gov Database
Bibliographic record
Abstract
BACKGROUND: The therapeutic challenge of glioblastoma (GBM) has catalyzed the development of clinical trials to evaluate novel interventions. With increased understanding of GBM biology and technological advances, the neurosurgeon's role in neuro-oncology has evolved. OBJECTIVE: To evaluate the current landscape of procedure-based clinical trials for GBM to characterize this evolution, gain insight into past failures, and accordingly outline implications for future research and practice that may inform future studies. METHODS: The ClinicalTrials.gov database was searched for surgical/procedural trials in individuals with GBM. Demographics, specific intervention, trial phase, and main outcome measures were abstracted. RESULTS: A total of 224 of 2311 GBM trials (9.7%) were identified as procedural, with the majority being based in the United States (155/224, 69.2%), single-center (155/224, 69.2%), and not randomized (176/224, 78.6%). Primary and recurrent GBMs were evenly addressed. The leading interventions were local delivery of therapeutics (50.0%), surgical techniques (33.9%), such as image-guided surgery, and novel device applications (14.3%). Phase I designs predominated (82/224, 36.6%). The top primary outcome was safety/tolerability/feasibility (88/224, 39.3%), followed by survival (46/224, 20.5%). Approximately 17% of studies were terminated, withdrawn, or suspended. Fifty-two linked publications were identified, among which 42 were classified as having a positive result. CONCLUSION: Procedural interventions comprised ∼10% of all registered GBM trials. Local delivery of therapeutics, use of surgical imaging techniques and novel device applications, predominantly through phase I designs, represent the evolved role of the neurosurgeon in neuro-oncology. Improved reporting of trial designs, outcomes, and results are needed to better inform the field and increase efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".