Fatigue and cognitive dysfunction in previously hospitalized patients with COVID-19: A 1-year follow-up
Bibliographic record
Abstract
PURPOSE: The aim was to longitudinally explore changes in fatigue- and cognition-related symptoms during the first year after hospital treatment for COVID-19. METHOD: Patients hospitalized for COVID-19 in Gothenburg, Sweden, were consecutively included from 01-07-2020 to 28-02-2021. Patients were assessed at the hospital (acute) and at 3 and 12 months after hospital discharge. Cognition was assessed with the Montreal Cognitive Assessment (MoCA), the Trail Making Test B (TMTB), and the Cognitive Failure Questionnaire (CFQ). Fatigue was assessed using the Multidimensional Fatigue Inventory-20 (MFI-20) and the Mental Fatigue Scale (MFS). Data was analyzed with demographics and changes over time calculated with univariable mixed-effects models. RESULT: In total, 122 participants were included. Analyzes of Z-scores for MoCA indicated improvement over the year, however the results were 1 SD below norm at all assessments. Alertness (TMTB scores) improved significantly from the acute assessment to the 12- month follow-up (p = <0.001, 95% CI 34.67-69.67). CFQ scores indicated cognitive impairment, and the sum scores for MFI reflected a relatively high degree of fatigue at follow-up. CONCLUSION: In the first year after hospitalization for COVID-19, most patients experienced fatigue and cognitive impairment. Alertness improved, but improvements in other domains were limited.
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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 | 0.001 |
| 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 | 0.000 | 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".