Review: donepezil improves cognitive and global function in people with mild to moderate Alzheimer’s disease
Bibliographic record
Abstract
Whitehead A, Perdomo C, Pratt RD, et al . Donepezil for the symptomatic treatment of patients with mild to moderate Alzheimer’s disease: a meta-analysis of individual patient data from randomised controlled trials. Int J Geriatr Psychiatry 2004;19:624–33.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q Does donepezil improve cognitive and global function in people with mild to moderate Alzheimer’s disease? ### ![Graphic][5] Design: Systematic review with meta-analysis. ### ![Graphic][6] Data sources: Studies were identified using the Eisai Inc database and from scientific literature searches (search dates not stated). ### ![Graphic][7] Study selection and analysis: Eligible studies were randomised controlled trials comparing donepezil versus placebo in people with mild to moderate Alzheimer’s disease (MMSE criteria). Trials were undertaken and completed by 20 December 1999. Exclusions: not stated. Data were extracted for each individual study on the efficacy and safety of donepezil. Meta-analysis was carried out using fixed and random effects models, and results were tested for statistical heterogeneity. ### ![Graphic][8] Outcomes: Change in cognitive status (Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog)); improvement in global function (Clinician’s … [1]: {openurl}?query=rft.jtitle%253DInternational%2Bjournal%2Bof%2Bgeriatric%2Bpsychiatry%26rft.stitle%253DInt%2BJ%2BGeriatr%2BPsychiatry%26rft.aulast%253DWhitehead%26rft.auinit1%253DA.%26rft.volume%253D19%26rft.issue%253D7%26rft.spage%253D624%26rft.epage%253D633%26rft.atitle%253DDonepezil%2Bfor%2Bthe%2Bsymptomatic%2Btreatment%2Bof%2Bpatients%2Bwith%2Bmild%2Bto%2Bmoderate%2BAlzheimer%2527s%2Bdisease%253A%2Ba%2Bmeta-analysis%2Bof%2Bindividual%2Bpatient%2Bdata%2Bfrom%2Brandomised%2Bcontrolled%2Btrials.%26rft_id%253Dinfo%253Adoi%252F10.1002%252Fgps.1133%26rft_id%253Dinfo%253Apmid%252F15254918%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1002/gps.1133&link_type=DOI [3]: /lookup/external-ref?access_num=15254918&link_type=MED&atom=%2Febmental%2F8%2F1%2F15.atom [4]: /lookup/external-ref?access_num=000222516600002&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.005 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".