Cognitive sequelae of long COVID may not be permanent: A prospective study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive decline is a recognized manifestation of long COVID, even among patients who experience mild disease. However, there is no evidence regarding the length of cognitive decline in these patients. This study aimed to assess whether COVID-19-related cognitive decline is a permanent deficit or if it improves over time. METHODS: Cognitive performance was evaluated by means of the Montreal Cognitive Assessment (MoCA) in COVID-19 survivors and noninfected individuals. All study participants had four cognitive evaluations, two of them before the pandemic and the other two, 6 and 18 months after the initial SARS-CoV-2 outbreak infection in the village. Linear mixed effects models for longitudinal data were fitted to assess differences in cognitive performance across COVID-19 survivors and noninfected individuals. RESULTS: The study included 78 participants, 50 with history of mild COVID-19 and 28 without. There was a significant-likely age-related-decline in MoCA scores between the two prepandemic tests (β = -1.53, 95% confidence interval [CI] = -2.14 to -0.92, p < 0.001), which did not differ across individuals who later developed COVID-19 when compared to noninfected individuals. Six months after infection, only COVID-19 survivors had a significant decline in MoCA scores (β = -1.37, 95% CI = -2.14 to -0.61, p < 0.001), which reversed after 1 additional year of follow-up (β = 0.66, 95% CI = -0.11 to 1.42, p = 0.092). No differences were noticed among noninfected individuals when both postpandemic MoCA scores were compared. CONCLUSIONS: Study results suggest that long COVID-related cognitive decline may spontaneously improve 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".