Progression of Cognitive Deficit in Older People with Mild Cognitive Impairment Treated with Cerebrolysin
Bibliographic record
Abstract
Objectives: Although people with amnestic mild cognitive impairment (aMCI) benefit from cerebrolysin treatment, some still develop dementia. The aim of the current study was to identify most informative clinical assessment tests to predict the therapy efficacy in aMCI subjects treated with cerebrolysin. Methods: We studied patients with amnestic mild cognitive impairment (aMCI; n = 53) who had regular neurocognitive and clinical psychiatric assessments and were treated with cerebrolysin i.v. infusions 20 × 30 ml twice a year over three years period. Data were analyzed using non-parametric statistics, cluster and linear discriminant analyses. Results: Combined mathematical modeling enabled to predict cognitive decline from aMCI to dementia in the cerebrolysin-treated patients based on their initial neurocognitive assessment scores. We identified a “dementia risk group” with fast cognitive decline (i.e. low efficacy of the treatment). Lower baseline scores in the Mattis Dementia Rating Scale Memory subtest, Mini-Mental State Examination (MMSE), 10 word list immediate recall, and Frontal Assessment Battery tests when accompanied by higher depression score (Hamilton Depression Rating Scale) suggest poor prognosis for aMCI patients treated with cerebrolysin. Changes in scores on the MMSE, Boston naming test, Digit span forward, and Wechsler scale subtest “Categorical associations” during the treatment course are more characteristic for patients who convert to dementia than their initial scores. Conclusions: aMCI subjects treated with cerebrolysin with lower baseline cognitive functioning and subclinical depression have poor prognosis in terms of converting to dementia. Changes in the MMSE, Boston naming test, Digit span forward, and Wechsler scale subtest “Categorical associations” scores during the treatment course are more informative to identify patients who will develop dementia than their initial scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".