Medical and Paramedical Care of Patients With Cerebellar Ataxia During the COVID-19 Outbreak: Seven Practical Recommendations of the COVID 19 Cerebellum Task Force
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), the cause of the current pandemic coronavirus disease 2019 (COVID-19), primarily targets the respiratory system. Some patients also experience neurological signs and symptoms ranging from anosmia, ageusia, headache, nausea, and vomiting to confusion, encephalitis, and stroke. Approximately 36% of those with severe COVID-19 experience neurological complications. The virus may enter the central nervous system through the olfactory nerve in the nasal cavity and damage neurons in the brainstem nuclei involved in the regulation of respiration. Patients with cerebellar ataxia (CA) are particularly vulnerable to severe outcome if they contract COVID-19 because of the complexity of their disease, the presence of comorbidities, and their use of immunosuppressive therapies. Most CA patients burdened by progressive neurologic deficits have substantially impaired mobility and other essential functions, for which they rely heavily on ambulatory services, including rehabilitation and psychosocial care. Cessation of these interventions because of isolation restrictions places the CA patient population at risk of further deterioration. This international panel of ataxia experts provides recommendations for neurologists caring for patients with CA, emphasizing a pro-active approach designed to maintain their autonomy and well-being: continue long-term medications, promote rehabilitation efforts, utilize the technology of virtual visits for regular contact with healthcare providers, and pay attention to emotional and psychosocial health. Neurologists should play an active role in decision-making in those CA cases requiring escalation to intensive care and resuscitation. Multi-disciplinary collaboration between care teams is always important, and never more so than in the context of the current pandemic.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".