MétaCan
Menu
Back to cohort
Record W2581475074

Driving and dementia: Efficient approach to driving safety concerns in family practice.

2017· article· en· W2581475074 on OpenAlexaff
Linda Lee, Frank Molnar

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of OttawaMcMaster UniversityCentre for Family Medicine
Fundersnot available
KeywordsDementiaCognitionAffect (linguistics)AccreditationApplied psychologyPatient safetyHuman factors and ergonomicsPrimary careOccupational safety and healthMedicinePoison controlNursingPsychologyHealth careMedical emergencyMedical educationFamily medicinePsychiatryDisease
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide primary care physicians with an approach to driving safety concerns when older persons present with memory difficulties. SOURCES OF INFORMATION: The approach is based on an accredited memory clinic training program developed by the Centre for Family Medicine Primary Care Collaborative Memory Clinic. MAIN MESSAGE: One of the most challenging aspects of dementia care is the assessment of driving safety. Drivers with dementia are at higher risk of motor vehicle collisions, yet many drivers with mild dementia might be safely able to continue driving for several years. Because safe driving is dependent on multiple cognitive and functional skills, clinicians should carefully consider many factors when determining if cognitive concerns affect driving safety. Specific findings on corroborated history and office-based cognitive testing might aid in the physician's decisions to refer for comprehensive on-road driving evaluation and whether to notify transportation authorities in accordance with provincial reporting requirements. Sensitive communication and a person-centred approach are essential. CONCLUSION: Primary care physicians must consider many factors when determining if cognitive concerns might affect driving safety in older drivers.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.081
GPT teacher head0.379
Teacher spread0.298 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicOlder Adults Driving StudiesFrench-language works237,207