Neuropalliative care for the neurosurgeon: a primer
Bibliographic record
Abstract
Many neurosurgical conditions are incurable, leading to disability or severe symptoms, poor quality of life, and distress for patients and families. The field of neuropalliative care (NPC) addresses the palliative care (PC) needs of individuals living with neurological conditions. Neurosurgeons play an important role within multidisciplinary NPC teams because of their understanding of the natural history of and treatment strategies for neurosurgical conditions, longitudinal patient-physician relationships, and responsibility for neurosurgical emergencies. Moreover, patients with neurosurgical conditions have unique PC needs given the trajectories of neurosurgical diseases, the realities of prognostication, psychosocial factors, communication strategies, and human behavior. PC improves outcomes among neurosurgical patients. Despite the importance of NPC, neurosurgeons often lack formal training in PC skills, which include identifying patients who require PC, assessing a patient's understanding and preferences regarding illness, educating patients, building trust, managing symptoms, addressing family and caregiver needs, discussing end-of-life care, and recognizing when to refer patients to specialists. The future of NPC involves increasing awareness of the approach's importance, delineating priorities for neurosurgeons with regard to NPC, increasing emphasis on PC skills during training and practice, expanding research efforts, and adjusting reimbursement structures to incentivize the provision of NPC by neurosurgeons.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".