Profiling Cognitive Impairment in Mild COVID-19 Patients
Bibliographic record
Abstract
Context: COVID-19 pandemic continues to be a serious threat to humanity even after the last 2.5 years and multiple reported waves. Post-COVID-19 cognitive impairment has a detrimental effect on the quality of life, education, occupation, psychosocial as well as adaptive functioning and independence. Aims and Objective: Profiling the cognitive impairment in the mild COVID-19 recovered patients. Settings and Design: Interview-based case-control study. Materials and Methods: This study was conducted at a secondary healthcare center in a hilly region of north India. Group A included mild COVID-19 recovered patients and Group B included local non-COVID healthy individuals. Both groups of participants were interviewed using Montreal Cognitive Assessment (MoCA) to identify global and domain-wise cognitive impairment. Statistics Used: Descriptive statistics were used to analyze the demographic and clinical variables. The Chi-square test was used to evaluate these results and statistical analysis was done using the Statistical Package for Social Sciences (version 23) program. Results: A total of 284 individuals were enrolled in our study, equally split into Groups A (cases) and B (controls). No global cognitive decline was found in any participant. However, 40 cases scored low on MoCA. The decrease in domain-wise cognitive function was statistically significant for visuospatial skill/executive function and attention. Conclusion: Our results have demonstrated that there is domain-wise cognitive impairment associated with mild COVID-19 disease. We recommend lowering the threshold of the MoCA to identify the early cognitive impairment and the inclusion of detailed cognitive assessment in post-COVID-19 follow-ups to initiate early cognitive rehabilitation among these patients.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".