Optimising Outcomes for Glioblastoma through Subspecialisation in a Regional Cancer Centre
Bibliographic record
Abstract
Delivery of highly sophisticated, and subspecialised, management protocols for glioblastoma in low volume rural and regional areas creates potential issues for equivalent quality of care. This study aims to demonstrate the impact on clinical quality indicators through the development of a novel model of care delivering an outsourced subspecialised neuro-oncology service in a regional centre compared with the large volume metropolitan centre. Three hundred and fifty-two patients with glioblastoma were managed under the European Organisation for Research and Treatment of Cancer and National Cancer Institute of Canada Clinical Trials Group (EORTC-NCIC) Protocol, and survival outcome was assessed in relation to potential prognostic factors and the geographical site of treatment, before and after opening of a regional cancer centre. The median overall survival was 17 months (95% CI: 15.5–18.5), with more favourable outcome with age less than 50 years (p < 0.001), near-total resection (p < 0.001), Eastern Cooperative Oncology Group (ECOG) Performance status 0, 1 (p < 0.001), and presence of O-6 methylguanine DNA methyltransferase (MGMT) methylation (p = 0.001). There was no difference in survival outcome for patients managed at the regional centre, compared with metropolitan centre (p = 0.35). Similarly, no difference was seen with clinical quality process indicators of clinical trial involvement, rates of repeat craniotomy, use of bevacizumab and re-irradiation. This model of neuro-oncology subspecialisation allowed equivalent outcomes to be achieved within a regional cancer centre compared to large volume metropolitan centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".