Neurological outcomes 1 year after COVID‐19 diagnosis: A prospective longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Neurological sequelae from coronavirus disease 2019 (COVID-19) may persist after recovery from acute infection. Here, the aim was to describe the natural history of neurological manifestations over 1 year after COVID-19. METHODS: A prospective, multicentre, longitudinal cohort study in COVID-19 survivors was performed. At a 3-month and 1-year follow-up, patients were assessed for neurological impairments by a neurological examination and a standardized test battery including the assessment of hyposmia (16-item Sniffin' Sticks test), cognitive deficits (Montreal Cognitive Assessment < 26) and mental health (Hospital Anxiety and Depression Scale and Post-traumatic Stress Disorder Checklist 5). RESULTS: Eighty-one patients were evaluated 1 year after COVID-19, out of which 76 (94%) patients completed a 3-month and 1-year follow-up. Patients were 54 (47-64) years old and 59% were male. New and persistent neurological disorders were found in 15% (3 months) and 12% (10/81; 1 year). Symptoms at 1-year follow-up were reported by 48/81 (59%) patients, including fatigue (38%), concentration difficulties (25%), forgetfulness (25%), sleep disturbances (22%), myalgia (17%), limb weakness (17%), headache (16%), impaired sensation (16%) and hyposmia (15%). Neurological examination revealed findings in 52/81 (64%) patients without improvement over time (3 months, 61%, p = 0.230) including objective hyposmia (Sniffin' Sticks test <13; 51%). Cognitive deficits were apparent in 18%, whereas signs of depression, anxiety and post-traumatic stress disorders were found in 6%, 29% and 10% respectively 1 year after infection. These mental and cognitive disorders had not improved after the 3-month follow-up (all p > 0.05). CONCLUSION: Our data indicate that a significant patient number still suffer from neurological sequelae including neuropsychiatric symptoms 1 year after COVID-19 calling for interdisciplinary management of these patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".