“More Areas of Grey”
Bibliographic record
Abstract
Neuropalliative care as a clinical speciality aims to address the unique end-of-life needs and concerns of patients with neurologic disease. Although literature has outlined clinical hurdles, a more nuanced understanding of how neuropalliative care was experienced, conceptualized, and enacted could provide context and depth to better outline practice and research priorities. This article presents findings from an ethnographic study of neuropalliative care conducted in a university-affiliated, tertiary care neurological hospital in Canada with a dedicated neuropalliative consultation service. Specifically, this article examines how clinical hurdles outlined in the neuropalliative literature were experienced and addressed by multiple stakeholders, including patients, families, and clinicians. These clinical hurdles include locating the scope of neuropalliative care, ascertaining the impact of prognostic uncertainty and poor recognition of the dying patient, and navigating the tensions between curative and palliative philosophies. In the discussion, the implications of these clinical hurdles are addressed, concluding with reflections on the role of ethnography, palliative care in the context of functional changes, and broadening approaches to uncertainty.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.119 | 0.011 |
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".