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Record W2095247682 · doi:10.1097/jcp.0b013e31822bb1ba

The Effects of Donepezil on Computer-Simulated Driving Ability Among Healthy Older Adults

2011· article· en· W2095247682 on OpenAlexafffund
Mark Rapoport, Bruce Weaver, Alex Kiss, Carla Zucchero Sarracini, Henry J. Moller, Nathan Herrmann, Krista L. Lanctôt, Brian J. Murray, Michel Bédard

Bibliographic record

VenueJournal of Clinical Psychopharmacology · 2011
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersH. Lundbeck A/SAlzheimer SocietyPfizerAbbott Laboratories
KeywordsDonepezilAnalysis of covariancePlaceboDriving simulatorCognitionPsychologyAudiologyCognitive declinePhysical medicine and rehabilitationMedicineSimulationPhysical therapyDementiaStatisticsPsychiatryInternal medicineComputer scienceMathematicsDisease

Abstract

fetched live from OpenAlex

The purpose of the present pilot study was to examine the effect of donepezil on simulated driving among healthy older adults. Twenty participants with a mean age of 72 years were randomized to take 5 mg of donepezil or placebo for 2 weeks. All participants were assessed at baseline and 2 weeks later on measures of attention, global cognition, and simulated driving on the York driving simulator. Driving measures included speed deviation, deviation of road position, reaction time to wind gusts, and collisions. Groups were compared using analysis of covariance, controlling for baseline values. There were no differences between the groups on attentional measures, number of collisions, or composite simulator measures. The placebo group fared approximately 0.5 second better in reaction time to wind gusts and showed a nonsignificant tendency toward less deviation of road position, compared with the donepezil group. This analysis does not support the use of donepezil to extend the period of safe driving among older adults, but further study is needed regarding its role among patients with cognitive disorders.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.499
Teacher spread0.434 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2011
Admission routes2
Has abstractyes

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