COVID-19 and Integrated Multidisciplinary Care Model in Parkinson’s Disease: Literature Review & Future Perspectives
Bibliographic record
Abstract
Clinical diversity and multi-systemic manifestations of Parkinson's disease (PD) necessitate the involvement of several healthcare professionals from different disciplines for optimal care. Clinical guidelines recommend that all persons with PD should have access to a broad range of medical and allied health professionals to implement an efficient and effective multidisciplinary care model. This is well supported by growing evidence showing the benefits of multidisciplinary interventions on improving quality of life and disease progression in PD. However, a "multidisciplinary" approach requires gathering healthcare professionals from different disciplines into an integrative platform for collaborative teamwork. With the Coronavirus Disease 2019 (COVID-19) pandemic, implementation of such a multidisciplinary care model has become increasingly challenging due to social distancing mandates, isolation and quarantine, clinics cancellation, among others. To address this problem, multidisciplinary teams are developing innovate virtual platforms to maintain care of people with PD. In the present review, we cover aspects on how SARS-CoV-2 has affected people with PD, their caregivers, and care team members. We also review current evidence on the importance of maintaining patient-centered care in the era of social distancing, and how can we utilize telehealth and innovative virtual platforms for multidisciplinary care in PD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".