Recommendations for the Organization of Multidisciplinary Clinical Care Teams in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Optimal management in expert centers for Parkinson's disease (PD) usually involves pharmacological and non-pharmacological interventions, delivered by a multidisciplinary approach. However, there is no guideline specifying how this model should be organized. Consequently, the nature of multidisciplinary care varies widely. OBJECTIVE: To optimize care delivery, we aimed to provide recommendations for the organization of multidisciplinary care in PD. METHODS: Twenty expert centers in the field of multidisciplinary PD care participated. Their leading neurologists completed a survey covering eight themes: elements for optimal multidisciplinary care; team members; role of patients and care partners; team coordination; team meetings; inpatient versus outpatient care; telehealth; and challenges towards multidisciplinary care. During a consensus meeting, outcomes were incorporated into concept recommendations that were reviewed by each center's multidisciplinary team. Three patient organizations rated the recommendations according to patient priorities. Based on this feedback, a final set of recommendations (essential elements for delivery of multidisciplinary care) and considerations (desirable elements) was developed. RESULTS: We developed 30 recommendations and 10 considerations. The patient organizations rated the following recommendations as most important: care is organized in a patient-centered way; every newly diagnosed patient has access to a core multidisciplinary team; and each team has a coordinator. A checklist was created to further facilitate its implementation. CONCLUSION: We provide a practical tool to improve multidisciplinary care for persons with PD at the organizational level. Future studies should focus on implementing these recommendations in clinical practice, evaluating their potential applicability and effectiveness, and comparing alternative models of PD care.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".