"COGNITIVE FUNCTIONS AND HEALTH STATUS IN PREVIOUSLY HOSPITALIZED VERSUS NON-HOSPITALIZED POST-COVID PATIENTS: A PILOT STUDY"
Bibliographic record
Abstract
Introduction: An increasing amount of evidence indicates that patients after having been recovered from COVID-19 are still experiencing various neurocognitive deficits, as part of the commonly described post-COVID sequalae.These deficits can significantly affect patients' perceived health status and quality of life.Method: This study assesses global cognitive functions, executive functions, memory, attention, subjective cognitive functioning, perceived health status and quality of life.We compared 16 previously hospitalized (56% males, 44% females, age: 46.1±11.5 years, education in years: 14.9±4.2years) and 16 non-hospitalized (50% males age: 46.8±15.7 years, education in years: 14.8±2.38 years) post-COVID patients who were matched based on age, education level and time elapsed since the end of COVID-19 infection (respectively 160±140 166±143 days).Objective neuropsychological tests such as the Montreal Cognitive Assessment (MoCA), forward and backward digit spans, Trail Making Test (TMT), verbal fluency test, Frontal Assessment Battery (FAB) and self-report scales such as the Perceived Deficits Questionnaire (PDQ), Post-COVID-19 Functional Scale (PCFS) and EQ-VAS were administered and analyzed.Results: Previously hospitalized patients performed significantly worse on forward digit span task (p<.05), backward digit span task (p<.05) and lexical fluency (p<.05) compared to non-hospitalized patients.Moreover, hospitalized patients showed significantly higher scores on both parts of PCFS (p<.01 and p<.05).Discussion: This pilot study found that previously hospitalized post-COVID patients demonstrate decreased short-term memory, working memory, lexical fluency and perceived quality of life compared to previously non-hospitalized post-COVID patients.Nevertheless, contrasting to previous literature, we did not find significant differences in global cognition, mental flexibility, overall frontal functions, subjective cognitive deficit, and perceived health status.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".