Slow but Evident Recovery from Neocortical Dysfunction and Cognitive Impairment in a Series of Chronic COVID-19 Patients
Bibliographic record
Abstract
Cognitive impairment is a frequent complaint in coronavirus disease 2019 (COVID-19) and can be related to cortical hypometabolism on 18F-FDG PET at the subacute stage. However, it is unclear if these changes are reversible. Methods: We prospectively assessed the Montreal Cognitive Assessment scores and 18F-FDG PET scans of 8 COVID-19 patients at the subacute stage (once no longer infectious) and the chronic stage (˜6 mo after symptom onset). The expression of the previously established COVID-19–related covariance pattern was analyzed at both stages to examine the time course of post–COVID-19 cognitive impairment. For further validation, we also conducted a conventional group analysis. Results: Follow-up 18F-FDG PET revealed that there was a significant reduction in the initial frontoparietal and, to a lesser extent, temporal hypometabolism and that this reduction was accompanied by a significant improvement in cognition. The expression of the previously established COVID-19–related pattern was significantly lower at follow-up and correlated inversely with Montreal Cognitive Assessment performance. However, both 18F-FDG PET and cognitive assessment suggest a residual impairment. Conclusion: Although a significant recovery of regional neuronal function and cognition can be clearly stated, residuals are still measurable in some patients 6 mo after manifestation of COVID-19. Given the current pandemic situation and tremendous uncertainty concerning the long-term effects of COVID-19, the present study provides novel insights of the highest medical and socioeconomic relevance.
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 | 0.000 | 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) | 0.001 | 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".