P15.22.B UNMASKING SOCIOECONOMIC INEQUITIES IN GLIOBLASTOMA CARE: A SYSTEMATIC REVIEW OF BARRIERS TO PALLIATIVE AND SUPPORTIVE SERVICES IN ASIA
Bibliographic record
Abstract
Abstract BACKGROUND Glioblastoma (GBM), the most aggressive primary brain tumor, imposes severe physical, emotional, and financial burdens, especially in low- and middle-income countries (LMICs), where socioeconomic disparities restrict access to palliative and supportive care services that are essential for quality of life (QoL). This systematic review presents the first comprehensive synthesis of GBM care barriers in Asia, focusing on structural inequities and their impact on symptom management, psychosocial support, and end-of-life care. MATERIAL AND METHODS A systematic search of PubMed, Cochrane, Scopus, ScienceDirect, and Wiley Online Library (2000 to 2025) identified studies on GBM treatment access, socioeconomic factors, and outcomes in Asia and LMICs. Inclusion criteria targeted studies addressing income, education, insurance status, treatment adherence, and access to palliative care. Twenty-six studies were included and appraised using the CHEERS checklist and Newcastle-Ottawa Scale. A thematic synthesis was conducted to identify common barriers, systemic gaps, and implications for QoL and supportive care. RESULTS Socioeconomic disparities consistently increased symptom burden and delayed access to supportive care, reducing QoL. In the Philippines, 62 percent of patients discontinued adjuvant therapy due to financial constraints, with a median survival of 7.6 months and limited access to pain relief or psychological support. Uninsured patients had shorter survival (8.8 versus 15.2 months), exacerbating emotional distress and caregiver strain. In Taiwan, universal health coverage improved therapy access, but supportive care gaps persisted. Systemic barriers such as a significant neurosurgeon shortage in South Asia and limited availability of genomic diagnostics (only 9.4 percent of LMIC centers offered next-generation sequencing) hindered timely, personalized care. Financial toxicity frequently led to treatment abandonment and unmanaged end-of-life symptoms. CONCLUSION Socioeconomic inequities in Asia and LMICs critically limit access to palliative and supportive services for GBM patients, increasing distress and reducing QoL. Policy solutions should include expansion of publicly funded therapies, enhancement of neurosurgical and diagnostic capacity, and implementation of universal health coverage with strong financial protection to support equitable, patient-centered GBM care.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".