The association between objective cognitive measures and ecological-functional outcomes in COVID-19
Bibliographic record
Abstract
Background Cognitive dysfunctions, both subjective and detectable at psychometric testing, may follow SARS-CoV-2 infection. However, the ecological-functional relevance of such objective deficits is currently under-investigated. This study thus aimed at investigating the association between objective cognitive measures and both physical and cognitive, ecological-functional outcomes in post-COVID-19. Methods Forty-two COVID-19-recovered individuals were administered the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The Functional Independence Measure (FIM) was adopted to assess functional-ecological, motor/physical (FIM-Motor) and cognitive (FIM-Cognitive) outcomes at admission (T0) and discharge (T1). Results When predicting both T0/T1 FIM-total and-Motor scores based on MMSE/MoCA scores, premorbid risk for cognitive decline (RCD) and disease-related features, no model yielded a significant fit. However, the MoCA - but not the MMSE significantly predicted T0/T1 FIM-Cognitive scores. The MoCA was significantly related only to T0/T1 FIM-Cognitive Memory items. Discussion Cognitive measures are not associated with physical/motor everyday-life outcomes in post-COVID-19 patients. The MoCA may provide an ecological estimate of cognitive functioning in this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".