Cognitive Deficits in Patients with COVID-19 Infection during Their Hospital Stay: An Exploratory Study
Bibliographic record
Abstract
Abstract Background The literature on presence of cognitive deficits in patients recovered from coronavirus disease 2019 (COVID-19) infection is emerging. However, the data on whether cognitive deficits have its onset during the acute phase of illness has not been evaluated extensively. Aim This article estimates the level of cognitive functioning of patients with COVID-19 while they were admitted to COVID-designated wards. Secondary objectives were to assess the influence of medical comorbidities, severity of COVID-19 infection, and depressive and anxiety symptoms on cognitive functioning in patients with COVID-19 infection. Methods Sixty-six clinically stable patients with COVID-19 infection were evaluated during their inpatient stay on Hindi Montreal Cognitive Assessment scale (H-MoCA), Hindi Mini-Mental State Examination (HMSE) scale, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Questionnaire -7. Results The mean age of the study participants was 39.85 (standard deviation [SD] 16.89) years and the participants were evaluated after 9.34 (SD 4.98; median 9.0) days of being diagnosed with COVID-19 infection. About one-fourth (28.8%; n = 19) of the participants had cognitive impairment on HMSE and about two-fifths (n = 26; 39.39%) had cognitive impairment as per the cutoff used for H-MoCA. A higher level of cognitive deficits were seen among participants who were older, diagnosed with diabetes mellitus, and those who required oxygen support during their hospital stay prior to assessment. Conclusion Low cognitive score was found in one-fourth (28.8%) to two-fifths (39.9%) of the persons, depending on the assessment scale among those with acute COVID-19 infection. Low cognitive score was more prevalent among the elderly, those with diabetes mellitus, and those who required oxygen support prior to the assessment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".