Triggers for Referral to Specialized Palliative Care in Advanced Neurologic and Neurosurgical Conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background and Objectives: To systematically review the literature for the most suitable trigger criteria for referral to specialist palliative care services in life-limiting and life-threatening neurologic and neurosurgical conditions. Methods: was used to assess for risk of bias. Results: Our search identified 1,748 publications, of which 22 articles met the eligibility criteria. Studies were considered in 2 main groups: (A) studies designed specifically to identify trigger criteria for referral to specialized neuropalliative care services (n = 9) and (B) studies that retrospectively reported the reason for referral to specialized palliative care or reflected a consensus statement among people with advanced neurologic illness (n = 13). Overall, the results suggest that several published referral triggers for specialized neuropalliative care are based on expert consensus. However, there is a growing body of literature providing evidence-based condition-specific triggers for multiple sclerosis, parkinsonism, amyotrophic lateral sclerosis, and dementia. Discussion: There is a growing body of research that outlines evidence-based referral triggers for neuropalliative care. The ambiguity of nomenclature surrounding referral triggers in the current literature and field of neuropalliative care was a limitation to this study. We suggest that condition-specific triggers are likely to be the most effective for identifying the appropriate patients and timing for referral to specialist palliative care. (PROSPERO registration number: CRD42020135791, crd.york.ac.uk/prospero).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it