Glioblastoma, the Neurosurgeon, and Neuro-Palliative Care
Bibliographic record
Abstract
Primary malignant brain tumors, such as Glioblastoma multiforme (GBM), carry a poor survival prognosis and lead to a high burden of symptoms for the patient and his/her family.While advances in treatment modalities have led to increased survival rates for patients with GBM it remains a terminal diagnosis.Recent data has demonstrated that patients who receive early palliative care in addition to standard oncological therapies for non-neural cancers have a better quality of life, receive fewer aggressive treatments, and live significantly longer than those who do not receive early palliative care.To date, palliative care has arguably been an underutilized approach to assist in the management of GBM patients.Patients living with brain tumors have symptoms that differ from patients suffering with non-neural cancers, which raises the future possibility of physicians that are dedicated to palliative management of patients with neurological conditions, such as GBM.Common symptoms for patients actively dying with GBM include headaches, seizures, somnolence and fatigue, dysphagia, focal neurological deficits, and cognitive deficits.Neurosurgeons often have early contact with newly diagnosed brain tumor patients.Therefore, it is of utmost importance for the neurosurgeon to have an appreciation of palliative care for this specific patient population and to also have an understanding of the full spectrum of care that these patients will encounter while living, and dying due to GBM.The purpose of this paper is to review the existing literature surrounding palliative care for patients with malignant brain tumors particularly from the perspective of the neurosurgeon and because of the unique symptoms faced by this patient population introduce the concept of neuro-palliative 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".