Risk and course of COVID-19 in patients with Parkinson’s disease
Bibliographic record
Abstract
The article provides an overview of the data on the impact of Parkinson's disease on the risk of infection and the course of COVID-19, and also assesses the possible pathogenetic relationship between the SARS-CoV-2 virus, COVID-19 and PD. By penetrating the central nervous system, SARS-CoV-2 can cause not only neurological symptoms, but also exacerbate the course of an existing neurological disease. The impact of Parkinson's disease on the risk of infection and the course of COVID-19 is controversial. However, a number of authors support the opinion that PD is an anti-risk factor for the development of COVID-19, which is associated both with the pathogenesis of the disease and with the used antiparkinsonian drugs, in particular amantadines. There are no clear data indicating higher risk of infection and higher severity of COVID-19 in patients with PD. On the contrary, experimental and clinical data suggest a possible modifying role of α-synuclein and antiparkinsonian drugs.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".