Relationship between COVID‐19 and movement disorders: A narrative review
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The scientific literature on COVID-19 is increasingly growing. METHODS: In this paper, we review the literature on movement disorders in the context of the COVID-19 pandemic. RESULTS: First, there are a variety of transient movement disorders that may manifest in the acute phase of COVID-19, most often myoclonus, with more than 50 patients described in the literature. New onset parkinsonism, chorea, and tic-like behaviours have also been reported. Movement disorders as a side effect after COVID-19 vaccination are rare, occurring with a frequency of 0.00002-0.0002 depending on the product used, mostly manifesting with tremor. Current evidence for potential long-term manifestations, for example, long COVID parkinsonism, is separately discussed. Second, the pandemic has also had an impact on patients with pre-existing movement disorder syndromes, with negative effects on clinical status and overall well-being, and reduced access to medication and health care. In many parts, the pandemic has led to reorganization of the medical system, including the development of new digital solutions. The movement disorder-related evidence for this is reviewed and discussed. CONCLUSIONS: The pandemic and the associated preventive measures have had a negative impact on the clinical status, access to health care, and overall well-being of patients with pre-existing movement disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".