Donepezil in a chronic drug user—a potential treatment?
Bibliographic record
Abstract
The objective of the current study was to explore the potential cognitive benefits of an anticholinesterase inhibitor, donepezil, in a former chronic drug user. A neuropsychological test battery composed of the vocabulary and matrix reasoning subtests of the Wechsler adult intelligence scale-III, measures of everyday executive functioning (behavioural assessment of the dysexecutive syndrome [BADS]), and verbal learning and memory tasks (California verbal learning test-II; Rivermead behavioural memory test) was completed at baseline, at 3 months after introducing donepezil, and at 3 months after donepezil was discontinued. After donepezil treatment, substantial improvements were found on tasks of nonverbal fluid reasoning (i.e. matrix reasoning) and other executive functioning tests (i.e. BADS). At entry into the study, poor academic performance and subjective problems with memory and concentration were reported, particularly after amphetamine use (i.e. MDMA and crystal methamphetamine); after donepezil treatment, dramatic increases in memory, concentration and academic achievement were observed. The finding of improvements in tests of executive functioning and in academic performance in this case study, together with the minimal adverse side effects of donepezil, warrants the investigation of controlled studies of cholinergic enhancement in chronic amphetamine and other drug users.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".