Management of Advanced Therapies in Parkinson's Disease Patients in Times of Humanitarian Crisis: The <scp>COVID</scp>‐19 Experience
Bibliographic record
Abstract
BACKGROUND: Although the COVID-19 pandemic is affecting a relatively small proportion of the global population, its effects have already reached everyone. The pandemic has the potential to differentially disadvantage chronically ill patients, including those with Parkinson's disease (PD). The first health care reaction has been to limit access to clinics and neurology wards to preserve fragile patients with PD from being infected. In some regions, the shortage of medical staff has also forced movement disorders neurologists to provide care for patients with COVID-19. OBJECTIVE: To share the experience of various movement disorder neurologists operating in different world regions and provide a common approach to patients with PD, with a focus on those already on advanced therapies, which may serve as guidance in the current pandemic and for emergency situations that we may face in the future. CONCLUSION: Most of us were unprepared to deal with this condition given that in many health care systems, telemedicine has been only marginally available or only limited to email or telephone contacts. In addition, to ensure sufficient access to intensive care unit beds, most elective procedures (including deep brain stimulation or the initiation of infusion therapies) have been postponed. We all hope there will soon be a time when we will return to more regular hospital schedules. However, we should consider this crisis as an opportunity to change our approach and encourage our hospitals and health care systems to facilitate the remote management of chronic neurological patients, including those with advanced 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".