Neurological and Mental Health Symptoms Associated with Post-COVID-19 Disability in a Sample of Patients Discharged from a COVID-19 Ward: A Secondary Analysis
Bibliographic record
Abstract
Recent studies suggest that COVID-19 survivors may experience long-term health consequences: in particular, neurological and mental health symptoms might be associated with long-term negative outcomes. This study is a secondary analysis of a larger cohort study and aims to determine the extent to which neurological and mental health sequelae are associated with survivors' disability. Participants include COVID-19 survivors, with no pre-morbid brain conditions, who were discharged from the COVID-19 Unit of the ASST Spedali Civili Hospital between February and April 2020. At an average of 3.5 months after discharge, they were submitted to a neurological examination and completed the WHO Disability Assessment Schedule (WHODAS-12), the Hospital Anxiety and Depression Score, the Pittsburgh Sleep Quality Index and the Montreal Cognitive Assessment. Multivariable regression analysis was carried out to analyze variables that explain WHODAS-12 variation. In total, 83 patients (63 males, average age 66.9, 95% CI: 64.2-69.7) were enrolled; average WHODAS-12 was 13.2 (95% CI: 9.7-16.6). Cognitive dysfunction, anxiety, fatigue, and hyposmia/hypogeusia explained 28.8% of WHODAS-12 variation. These findings underline the importance and need for longitudinal follow-up assessments after recovery from COVID-19 and suggest the need for early rehabilitation of residual symptoms to enhance patients' functioning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".