SIX MONTHS FOLLOW-UP STUDY ON COGNITIVE PERFORMANCES IN PATIENTS WITH ISCHEMIC VASCULAR EVENTS
Bibliographic record
Abstract
Objective: The main aim of our study was to assess the cognitive state in patients after first ever stroke/transient ischemic attack (TIA). Material and methods: We studied a group composed of 74 patients admitted to Clinic of Neurology from Craiova for first ever stroke or TIA and also a group composed of 80 control subjects without signs of cerebrovascular disease (CVD) but with vascular risk factors present. To assess the cognitive state we tested the patients using Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA) at baseline, and after three and six months of follow-up. In both groups we performed brain computed tomography for a correct diagnosis. Results: At baseline the patient group showed a mean MMSE score 27.6 and a mean MoCA score 27.8. The control subjects showed a mean MMSE score 28.9 and a mean MoCA score 29. After six months the patient group showed a mean MMSE score 25.6 and MoCA score 20.1. The control subjects showed a mean MMSE score 26.9 and MoCA score 24.2. Conclusions: The patients with CVD showed a greater cognitive impairment than control subjects. In patients with stroke we observed a greater cognitive impairment than in the patients with TIA. In subjects with cerebrovascular risk factors there were signs of cognitive impairment even before the CVD became clinically evident.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".