Axial Improvement after Casirivimab/Imdevimab Treatment for COVID-19 in Parkinson’s Disease
Bibliographic record
Abstract
We report the case of a patient with advanced Parkinson's disease (PD) who developed significant improvement of freezing of gait (FOG) after receiving monoclonal antibodies cocktail casirivimab/Imdevimab (REGN-COV2, Regeneron-Roche) for the treatment of COVID-19 infection.A 70-year-old PD patient started at the age of 47 with rigidity and bradykinesia in the left foot.At 63, his symptoms were not well controlled with severe motor fluctuations, peak-dose dyskinesia, and levodopa-responsive FOG, so he was treated with bilateral subthalamic deep brain stimulation.His motor conditions considerably improved after surgery with the disappearance of motor fluctuations and FOG and marked reduction of dyskinesia.However, after 5 years, he progressively complained severe FOG, also after levodopa intake, with instability and rare falls together with mild peak-dose dyskinesia.In August 2021, he developed cough and myalgia and was diagnosed with mild COVID-19 infection after a positive nasopharyngeal swab for SARS-CoV-2.The patient was not vaccinated for COVID-19 before.In the very first days of infection, COVID-19 infection, PD symptoms did not worsen nor the patient required hospitalization.However, since the patient was considered to be at high risk of progressing to severe COVID-19 infection, he was immediately treated with a single intravenous infusion of REGN-COV2 (total dose: 2.4 g).No further drugs were administered except paracetamol.After REGN-COV2 infusion COVID-19 symptoms disappeared in few days.No changes were made to stimulation parameters or pharmacological daily treatment (levodopa/carbidopa 100/25 mg four tabs; rotigotine patch 6 mg; selegiline 5 mg).The day after REGN-COV2 infusion, he noticed a marked improvement of gait, particularly of FOG, and moderate improvement of speech.The benefits on walking remained unchanged for over 2 weeks (Video, segments 1, 2), then gradually reduced, and disappeared after about 40 days (Video, segment 3), as well as speech improvement.REGN-COV2 is an antibody cocktail containing two neutralizing human IgG1 SARS-CoV-2-neutralizing antibodies, recently
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".