Potential impact and challenges associated with Parkinson’s disease patient care amidst the COVID-19 global pandemic
Bibliographic record
Abstract
BACKGROUND: COVID-19 has made itself known to health care providers and families across the world in a matter of months. While primarily a respiratory disorder, it has also been shown to cause neurological symptoms, which can be a concern for Parkinson's disease (PD) patients. Although PD is not as common as other conditions such as cardiovascular diseases, it affects millions of patients around the world whose care has been affected by the global pandemic. OBJECTIVES: The aim of this review is to provide insight into the direct and indirect associations between COVID-19 and PD patient care. RESULTS: Potential direct effects of COVID-19 include possible neurodegeneration, concerns of symptom self-management with over-the-counter (OTC) products and ICU challenges that can arise in PD patients. In addition, a subset of PD patients may be at higher risk of severe COVID-19 infection. The indirect effects of the pandemic are associated with the social distancing measures and disruptions in health care systems and PD clinical trials, which may negatively affect PD patients' mental wellbeing and create barriers in controlling their PD symptoms. On a more positive note, telemedical care is quickly emerging as a primary communication tool for virtual patient care. However, further research should be conducted to examine the applicability of telemedicine across the entire PD population, such as those with more severe symptoms living in less developed areas. With all the uncertainty during this time, it is hopeful to hear many promising COVID-19 treatments being researched, one of them being a PD drug therapy, amantadine. CONCLUSION: Hopefully, we can consider this pandemic an opportunity to strengthen the PD community and learn more about the impact of the SARS-COV-2 virus. This review provides an overview of the interaction between COVID-19 and PD patients and future investigational retrospective studies are suggested to validate the observations.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".