Impact of COVID‐19 infection on cognition and its association with neurological symptoms
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To characterize the cognitive profile following COVID-19 infection and its possible association to clinical symptoms, emotional disturbance, biomarkers, and disease severity. METHODS: This was a single-center cross-sectional cohort study. Subjects between 20- and 60-year old with confirmed COVID-19 infection were included. Evaluation was performed between April 2020 and July 2021. Patients with previous cognitive impairment and other neurological or severe psychiatric disorders were excluded. Demographic and laboratory data were extracted from the medical records. RESULTS: Altogether 200 patients were included, 85 subjects were female (42.3%), and mean age was 49.12 years (SD: 7.84). Patients were classified into four groups: nonhospitalized (NH, n = 21), hospitalized without intensive care unit (ICU) nor oxygen therapy (HOSP, n = 42), hospitalized without ICU but with oxygen therapy (OXY, n = 107), and ICU (ICU, n = 31) patients. NH group was younger (p = .026). No significant differences were found in any test performed attending severity of illness (p > .05). A total of 55 patients reported subjective cognitive complaints (SCC). Subjects with neurological symptoms (NS) performed worse in trail making test B (p = .013), digits backwards (p = .006), letter&numbers (p = .002), symbol digit modalities test (p = .016), and Stroop color (p = .010) tests. CONCLUSIONS: OXY patients and females referred more SCC associated with symptoms of anxiety and depression. Objective cognitive performance was unrelated to SCC. No cognitive impairment was found regarding the severity of COVID-19 infection. Results suggest that NS such as headache, anosmia, and dysgeusia during infection were a risk factor for later cognitive deficits. Tests assessing attention, processing speed, and executive function were the most sensitive in detecting cognitive changes in these patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it