Evolution of Cognitive Disorders in Patients with Mild Cognitive Impairment (MCI) After Ischemic Stroke: Secondary Data Analysis from the Improved Health Care in Neurology and Psychiatry—Longer Life (IHCNP) Study
Bibliographic record
Abstract
Background: The Improved Health Care in Neurology and Psychiatry—Longer Life (IHCNP) study was an 18-month prospective, observational, non-interventional research study focused on patients with mild cognitive impairment (MCI) following ischemic stroke. Objectives: Our secondary analysis of the IHCNP data aimed to document the progression of MCI in this patient group. Methods: A total of 100 patients from Romania were recruited, all of whom underwent cognitive assessments using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Rey Auditory Verbal Learning Test (RAVLT). Clinical evaluations were also conducted as part of the study. Baseline cognitive scores were recorded, and subsequent follow-ups documented cognitive changes over time. Results: At baseline, cognitive scores indicated mild impairment, with averages of MMSE 25.41, MoCA 23.27, and RAVLT 33.63. By the end of the study, patients exhibited a significant cognitive decline, with MMSE scores dropping by 8.7%, MoCA by 10.0%, and RAVLT by 29.5% (p < 0.0001 for all measures), reflecting the progressive nature of MCI post-stroke. Conclusions: These findings highlight the importance of early diagnosis and intervention to mitigate cognitive decline in post-stroke patients. The study underscores the need for ongoing cognitive monitoring to improve patient outcomes and manage MCI progression effectively.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".