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Record W2325674445 · doi:10.1097/mrr.0000000000000150

Effects of an aerobic exercise program on driving performance in adults with cardiovascular disease

2016· article· en· W2325674445 on OpenAlexaff
Jeffrey Gaudet, Saïd Mekary, Mathieu Bélanger, Michel J. Johnson

Bibliographic record

VenueInternational Journal of Rehabilitation Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversité de MonctonAcadia UniversityVitalité Health NetworkUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineAerobic exerciseRehabilitationPhysical therapyDiseasePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) has been linked to decreases in driving performance and an increased crash risk. Regular exercise has been linked to improved driving performance among healthy adults. The aim of the current study was to investigate the relationship between a 12-week cardiac rehabilitation (CR) program and driving performance among individuals with CVD. Twenty-five individuals, including 12 cardiac adults and 13 healthy adults, took part in this study. Simulated driving performance was assessed using a standardized demerit-based scoring system at 0 and 12 weeks. Cardiac participants completed a 12-week CR program between evaluations. At baseline, cardiac participants had a higher number of demerit points than healthy adults (120.9±38.1 vs. 94.7±28.3, P=0.04). At follow-up, there was an improvement in both groups' driving evaluations, but the improvement was greater among the cardiac group such that there was no longer a difference in driving performance between both groups (94.6±30 vs. 86.9±34.8, P=0.51). Participation in an aerobic exercise-based CR program appears to lead to improvements in simulated driving performances of individuals with CVD.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.424
Teacher spread0.398 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2016
Admission routes1
Has abstractyes

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