Late Breaking Abstract - Fatigue, psychological and cognitive outcomes up to 12 months after hospitalization for COVID-19
Bibliographic record
Abstract
Patients with coronavirus disease 2019 (COVID-19) may experience long COVID, especially referring to persistent fatigue, but also other lingering symptoms as memory and concentration problems, anxiety, and depression. We aimed to evaluate the trajectories of fatigue and further cognitive and psychological recovery, and to assess the role of fatigue in the latter. In this prospective cohort study, we evaluated COVID-19 patients at 3, 6, and 12 months post-discharge with Montreal Cognitive Assessment (MoCA), and patient reported outcomes on fatigue, post-traumatic stress (PTSS), anxiety, and depression. We used linear mixed models for analyses. We have current data of 386 patients; 121 (32.4%) women, mean age 59.8±12.0 years, mean hospital stay 17.6±19.3 days. Fatigue was reported by 57.1%, 55.1%, and 54.9% of the patients; anxiety by 34.9%, 28.9%, and 22.0%; depression by 25.4, 24.7%, 17.6%; and PTSS by 12.7%, 10.8%, 5.5% at 3, 6, and 12 months, respectively. More than 90% of the cases of anxiety, depression, and PTSS was present in patients with fatigue. Fatigue was strongly associated with anxiety (p<0.001), depression (p<0.001), and PTSS (p<0.001). Fatigue (p=0.004), anxiety (p<0.001), depression (p=0.001), and PTSS (p<0.001) improved significantly over time up to 12 months. A deviating MoCA score was present in 44.8%, 36.8%, and 31.6% of the patients at 3, 6, and 12 months, respectively. A large proportion of COVID-19 patients reported significant fatigue, as well as psychological and cognitive problems. Recovery was observed over time, although fatigue showed only minimal improvement being present in 54.9% at 12 months. Fatigue seems to play a pivotal role in psychological recovery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".