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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".