DISP-10. The global landscape of glioblastoma clinical trials: A missing map for low- and middle-income countries
Bibliographic record
Abstract
Abstract BACKGROUND Glioblastoma is the most common primary CNS malignancy in adults with a median life expectancy of 11-12 months. Limited progress in treatment options and a poor prognosis have made glioblastoma a major focus in clinical trials. This study analyses the current clinical trial landscape and evaluates access to investigational treatments in low- and middle-income countries (LMICs). METHODS A comprehensive analysis was conducted using the ClinicalTrials.gov database, including all interventional trials on glioblastoma alone or with other solid tumors initiated since January 1, 2015. Trial phase, status, allocation, and site locations were extracted and analyzed using descriptive statistics. RESULTS As of the data cut-off on May 1, 2025, a total of 884 interventional clinical trials were identified. Among these, 55 trials were categorized as preclinical, 625 were early-phase studies, and only 54 were Phase 3 or 4 trials. At the time of analysis, 54.6% of the trials were stated as active, while 34.05% had either been completed or terminated. The primary objective in 88.24% of the studies was treatment, with the remainder focused on other interventions. Importantly, only 195 trials employed randomization. An analysis of trial site distribution revealed that only 192 clinical trials had sites located outside the U.S., Canada, Europe, and Australia and just 132 trials involved sites in LMICs. Among these, 113 trials were developed and conducted locally within a single LMIC, while only 19 were multicenter studies led by high-income countries. Importantly, when upper-middle-income countries were excluded, only 7 trials included sites in low- and lower-middle-income countries. CONCLUSIONS Despite the large number of clinical trials conducted globally, LMICs remain significantly underrepresented. This highlights a major global health issue, as patients diagnosed with glioblastoma in LMICs are often deprived from the access to investigational therapies. Urgent actions are required to expand clinical trial access in LMICs.
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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 | 0.091 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.037 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".