Co-ultraPEALut in Subjective Cognitive Impairment Following SARS-CoV-2 Infection: An Exploratory Retrospective Study
Bibliographic record
Abstract
Neurological involvement following coronavirus disease 19 (COVID-19) is thought to have a neuroinflammatory etiology. Co-ultraPEALut (an anti-inflammatory molecule) and luteolin (an anti-oxidant) have shown promising results as neuroinflammation antagonists. The aim of this study was to describe cognitive impairment in patients with post-COVID-19 treated with co-ultraPEALut. The Montreal Cognitive Assessment (MoCA), the Prospective–Retrospective Memory Questionnaire (PRMQ), the Fatigue Severity Scale (FSS), and a subjective assessment were administered at baseline and after 10 months. Patients treated with co-ultraPEALut were retrospectively compared with controls. Twenty-six patients treated with co-ultraPEALut showed a significant improvement in PRMQ (T0: 51.94 ± 10.55, T1: 39.67 ± 13.02, p < 0.00001) and MoCA raw score (T0: 25.76 ± 2.3, T1: 27.2 ± 2, p 0.0260); the MoCA-adjusted score and the FSS questionnaires also showed an improvement, even though it was not statistically significant; and 80.77% of patients reported a subjective improvement. In the control subjects (n = 15), the improvement was not as pronounced (PRMQ T0: 45.77 ± 13.47, T1: 42.33 ± 16.86, p 0.2051; FSS T0: 4.95 ± 1.57, T1: 4.06 ± 1.47, p 0.1352). Patients treated with co-ultraPEALut and corticosteroids were not statistically different from those treated with co-ultraPEALut alone. Neuro-post-COVID-19 patients treated with co-ultraPEALut scored better than controls in MoCA and PRMQ questionnaires after 10 months: this may support the importance of neuroinflammation modulation for neuro-long-COVID-19.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".