Bibliographic record
Abstract
A scan through recent literature in the two fields of palliative medicine and neurology suggests little overlap in their clinical practice. In the three major palliative care journals published, respectively, in UK, Canada and USA, the primary focus is advanced cancer. Fewer than 5% of papers deal with non-cancer conditions, and most often these are respiratory, cardiac and renal diseases, with only ALS representing neurology care. Similarly, published texts and articles in neurology concern themselves primarily with diagnosis, investigation and active treatment of disease, and include relatively little about end-of-life care and effective symptom management in advanced disease. A small number of exceptions exist. The publication in 2004 of the text ‘ Palliative Care and Neurology ’, a multiple author work coordinated by neurologist Raymond Volz, reflected a new awareness among neurologists that their responsibility in clinical care ought to extend beyond the major hospital, and ensure effective support and symptom management in home and chronic care settings. A little earlier, in 2001, an issue of Neurology Clinics was devoted entirely to palliative care. Although selective in the number of conditions it addressed, it represented a new direction in the field. There are readily discernible trends in modern neurology practice that will make it difficult for specialist neurologists to be active in promoting and engaging in the delivery of palliation support for persons affected by chronic neurological conditions.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.357 | 0.299 |
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".