Cognitive impairments in patients after COVID-19
Bibliographic record
Abstract
The purpose — to assess the severity of cognitive deficit in patients with previous COVID-19. Material and methods. We examined 20 patients after COVID-19 hospitalized in the Pulmonology Department of Republic Clinical Hospital of the Republic of Tatarstan. All patients underwent general clinical and laboratory examination, computed tomography of the lungs, and tests with a 6-minute walk. To assess the severity of dyspnea, we used the MMRS questionnaire and the Borg scale. We used the Montreal Cognitive Assessment Scale (MoCA), the Mini-Cog test, and the MMSE to identify cognitive functions. Results. After analyzing the data of patients with mild severity (CT 1), we found that cognitive functions and the volume of lung lesions did not have a significant relationship. In patients with large lung lesions (CT 2–4), there was a correlation between the detection rate and the percentage of frosted glass detection on the one hand and the results of the MiniCog test, on the other (r = -0,69 and r = -0,93). Also, in this group of patients, we found a strong positive correlation (r = -0,8) between the frosted glass ratio and the Borg score at rest. It shows that patients with smaller changes in the lungs assess the dyspnea severity more correctly. Conclusion. Patients after COVID-19 have cognitive impairments of varying severity. In the group of patients with more severe lung damage (CT 2–4), there are changes in cognitive functions that correlate with the functional status of the patient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".