Bibliographic record
Abstract
Clinical evidence is reviewed for the possible use of the adamantanes amantadine and memantine, for the prevention and/or treatment of COVID-19.Literature searches revealed three series of case reports authored by independent teams of international investigators.Subjects were comprised principally of patients receiving amantadine or memantine for several weeks as part of their treatment regimen for Parkinson's disease, Multiple Sclerosis or Cognitive impairment prior to their infection by SARS-CoV-2.All patients tested positive for SARS-CoV-2 confirmed by RT-PCR of nasopharyngeal swabs.Interestingly, the majority of cases manifested age-related vulnerabilities to COVID-19 as well as the presence of co-morbidities resulting from either severe neurological disorders or type-2 diabetes.Amantadine appeared to prevent the appearance of typical COVID-19-related clinical manifestations of infectious disease in 23/24 cases.In addition, one patient with type-2 diabetes treated with amantadine for 14 days showed clear improvements in clinical status and in oxygen saturation levels; by day 6 he could breathe without the need for oxygen supplementation and was discharged from hospital on day 14.It is necessary to now confirm these findings by randomized controlled trials in order to objectively evaluate the use of these agents for the prevention and/or treatment of COVID-19. Amantadine, Parkinson's Disease and COVID-19Amantadine is widely used for the treatment of Parkinson's
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".