P.020 Advanced care team for Parkinson’s: a novel approach for patients and carepartners in advanced parkinsonism
Bibliographic record
Abstract
Background: People with parkinsonian syndromes (PPS) in advanced stages deal with a wide range of highly impactful motor and non-motor problems, including dementia, hallucinations, falls, and dysautonomia. Care planning becomes difficult and unpredictable. In addition, while healthcare providers focus on reducing symptom burden, PPS and carepartners deal with difficult emotions such as demoralization and grief. At those stages, multidisciplinary care becomes imperative. In October 2022 we launched Advanced Care Team for Parkinson’s (ACT-PD), a clinical research program whose goals include advanced care planning, symptoms management and emotional support. Methods: Our primary outcomes are changes in quality of life (QoL-AD), carepartner burden (ZBI-12) and patient satisfaction. The team involves neurology, palliative nursing, social-work, psychology, and spiritual care. Every three months, participants meet the team in person or virtually. In two hours, they address tailored concerns, complemented with phone calls as required. Accordingly, participants complete assessments. Results: In its first 4 months, ACT-PD included 40 PPS and 40 carepartners. Preliminary results show that the first visit with ACT-PD resulted in a 30% reduction in carepartner burden and 28% of improvement in patients’ QoL. Conclusions: Even in early phases, this novel patient and carepartner-centered approach improves QoL and reduces carepartner burden in PPS in advanced disease stages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".