Predictors of Affective and Cognitive Alterations in Minor Stroke Patients: clinical, omics, neuropsychological and radiological signatures
Bibliographic record
Abstract
ABSTRACT Background. Despite the mild severity of initial symptoms, minor stroke (MS) is a common clinical condition that can be associated with significant affective and cognitive alterations. Methods. The PSICOICTUS project aimed to investigate the predictors of these alterations in a cohort of 118 consecutive MS patient. This observational, longitudinal, and prospective study included comprehensive evaluation at baseline (within the first five days of symptom onset) and follow-ups at 15 days, 6 months, and 12 months. A screening battery consisting of the Montreal Cognitive Assessment (MoCA), Montgomery-Åsberg Depression Rating Scale (MADRS), and Apathy Evaluation Scale-Clinician version (AES-c) was used to identify patients with affective and/or cognitive alterations. Results. Cognitive alterations were further assessed using a comprehensive neuropsychological battery. Screening tests revelated that 17.0% of patients had affective alterations, 9.3% had cognitive alterations, and 14.4% presented with both affective and cognitive alterations. Among patients with cognitive alterations, executive functions, attention and processing speed were found to be the most affected domains. The study also concluded a biomarker discovery analysis involving MRI-based neuroimaging and untargeted metabolomics/lipidomics analysis. Several predictors of affective and cognitive alterations were identified. A history of previous depressions, lower levels of Isoleucyl-Isoleucine, and PC (38:4) were associated with affective alterations. On the other hand, age above 70, the presence of a middle cerebral artery ischemic lesion, higher creatinine levels, lower triglyceride levels, and higher concentrations of 2-hydroxyhexadecanoylcarnitine were predictive of cognitive alterations. Female sex, previous history of depressive syndrome, and a higher number of chronic strokes were identified as predictors for both affective and cognitive alterations. Conclusions. These findings highlight the importance of considering affective and cognitive alterations in the management of patients with MS. The identification of specific predictors would improve the development of a differential management approach tailored to the needs of MS patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".