Evolving Trends in Neuropsychological Profiles of Post COVID-19 Condition: A 1-Year Follow-up in Individuals with Cognitive Complaints
Bibliographic record
Abstract
Background: Cognitive difficulties are reported as lasting sequelae within post COVID-19 condition. However, the chronicity of these difficulties and related factors of fatigue, mood, and perceived health have yet to be fully determined. More longitudinal studies are needed to clarify the trends of cognitive test performance and cognitive domain impairment following COVID-19 onset, and whether hospitalization influences outcomes. Methods: 57 participants who reported subjective cognitive difficulties after confirmed COVID-19 infection were assessed at baseline (~6 months post COVID-19) and follow-up (~15 months later) visits. Assessments included measures across multiple cognitive domains and self-report questionnaires of fatigue, mood, and overall health. Analyses were conducted in three stages: at the test score level (raw and adjusted scores), at the cognitive domain level, and stratified by hospitalization status during infection. Results: Impacts on cognitive test scores remain stable across assessments. Cognitive domain analyses indicate significant reductions in attention and executive functioning impairment, while memory impairment is slower resolve. On self-report measures, there was a significant improvement in overall health ratings at follow-up. Finally, those hospitalized during infection performed worse on timed cognitive measures across visits and accounted for a larger proportion of cases with short-term and working memory impairment at follow-up.Conclusions: Cognitive difficulties persist both at test score and cognitive domain levels in many cases of post COVID-19 condition, but evidence suggests some improvement in global measures of attention, executive functioning and overall self-rated health. An effect of hospitalization on cognitive symptoms post COVID-19 may be more discernible over time.
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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 | 0.002 |
| 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 | 0.001 | 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".