Covid-19 Analysis: Is there an Association Between Covid-19 and Development of Cognitive Deficits?
Bibliographic record
Abstract
Objective: The effects of COVID-19 infection were initially thought to be limited to the respiratory system; however, recent literature suggests that the virus has systemic effects, even leading to cognitive deficits. The objective of this study is to review COVID-19 related literature to determine whether there is an association between COVID-19 infection and the development of cognitive deficits. Method: A search for articles relevant to COVID-19, cognitive deficits, the Montreal Cognitive Assessment Tool (MoCA), and the geriatric population was performed on the MEDLINE, CINAHL, and APA PsychInfo databases. Results: Substantial evidence exists that reports an association between COVID-19 infection and cognitive decline. The studies included in this literature review surveyed distinct populations and reported cognitive deficits in COVID-19 patients as measured by a reduction in MoCA scores. While cognitive deficits were identified as partially reversible, there were still measurable deficits in cognition post-recovery compared to healthy controls. Furthermore, the measured cognitive deficits were found to be much worse in the geriatric population. Conclusions: Current literature shows an association between COVID-19 infection and the development of cognitive deficits. Further research should seek to characterize these cognitive deficits and determine the underlying aetiology and pathogenesis. Initiatives to develop interventions to limit or improve cognitive deficits in post COVID-19 patients is crucial, especially the elderly, given the large burden of disease within this population cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".