Triggers for Referral to Specialized Palliative Care in Advanced Neurologic and Neurosurgical Conditions
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).
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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 | 0.015 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".