Effects of Citicoline as an Adjunct Treatment for Alzheimer’s Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: A critical strategy in the management of Alzheimer's disease (AD) is optimizing the effects of currently available pharmacologic therapies such as citicoline (CC). OBJECTIVE: The purpose of this study was to determine the effects of CC as adjunct therapy to cholinesterase inhibitors (AChEI) in the treatment of AD. METHODS: We identified relevant studies by electronic search until April 2020. We considered studies with a comparator group that enrolled elderly patients with a diagnosis of AD and employed CC as an adjunct therapy to AChEIs compared to AChEI monotherapy or comparisons of different AChEIs combined with CC. Methodological quality assessment was done using the Newcastle-Ottawa Scale. RESULTS: Out of 149 articles identified, two retrospective cohort studies involving 563 elderly patients affected with AD were included. After 3 months and 9 months, better Mini-Mental Status Examination scores were observed in the "AChEIs + CC" group versus "AChEIs alone" group. CC combined with donepezil may be better in improving cognition than when combined with rivastigmine. No significant difference was noted in terms of activities of daily living (ADL) and instrumental-ADL. Neuropsychiatric Inventory and Geriatric Depression Scale-short form scores appeared to be lower in the combination treatment versus monotherapy. The adverse events of combined treatment were self-limiting and included occasional excitability, gastric intolerance, and headache. CONCLUSION: Limited evidence from pooled data of two observational studies suggests that CC used in adjunct with AChEIs in the treatment of AD was well-tolerated and showed improvement in cognition, mood, and behavioral symptoms compared to treating with AChEIs alone.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".