Advance Care Planning in Neurodegenerative Disorders: A Scoping Review
Bibliographic record
Abstract
Advance care planning (ACP) is increasingly acknowledged as a key step to enable patients to define their goals/preferences for future medical care, together with their carers and health professionals. We aimed to map the evidence on ACP in neurodegenerative disorders. We conducted a scoping review by searching PubMed (inception-December 28, 2020) in addition to trial, review, and dissertation registers. From 9367 records, we included 53 studies, mostly conducted in Europe (45%) and US-Canada (41%), within the last five years. Twenty-six percent of studies were qualitative, followed by observational (21%), reviews (19%), randomized controlled trials (RCTs, 19%), quasi-experimental (11%), and mixed-methods (4%). Two-thirds of studies addressed dementia, followed by amyotrophic lateral sclerosis (13%), and brain tumors (9%). The RCT interventions (all in dementia) consisted of educational programs, facilitated discussions, or videos for patients and/or carers. In conclusion, more research is needed to investigate barriers and facilitators of ACP uptake, as well as to develop/test interventions in almost all the neurodegenerative disorders. A common set of outcome measures targeting each discrete ACP behavior, and validated across the different diseases and cultures is also needed.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".