Integrating palliative medicine into neurology: description of a novel fellowship in neuropalliative care
Bibliographic record
Abstract
Background There is increasing collaboration between Neurology and Palliative Medicine in managing progressive neurological conditions. Integration is essential to deliver holistic care. Addressing physical, psychosocial and spiritual aspects of care, including advance care planning, enhances quality of life of these patients and their caregivers. Collaboration benefits service users, and also promotes interdisciplinary learning. In 2021, Ireland’s Health Service Executive, funded an inaugural Fellowship in Neuropalliative Care. Objective and Method Descriptive narrative of a pilot integrative service. Results The Fellow had extensive clinical exposure to a broad category of progressive neurological con- ditions, developing a better understanding of disease management, symptom burden, and associated challenges. Integration with the hospital Neurology service was accomplished through attending ward rounds and multidisciplinary team meetings. A Neuropalliative Care outpatient clinic was piloted, addressing burdensome symptoms and facilitating advance care planning. Initial challenges encountered included lack of expertise in neurological assessments, and unfamiliarity with medications prescribed in specialist clinics. To support service development, the Fellow linked with established Neuropalliative services to undertake observerships. Conclusions This approach allowed for rapid clinical exposure and integration within Neurology service. The development of relationships with centres with established integrative Neuropalliative services aided establishment of a novel service locally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".