MétaCan
Menu
Back to cohort
Record W2795960465 · doi:10.1038/s41598-018-22937-y

Driving habits and behaviors of patients with brain tumors: a self-report, cognitive and driving simulation study

2018· article· en· W2795960465 on OpenAlexafffund
Ann Mansur, Megan A. Hird, Alexa Desimone, Iryna Pshonyak, Tom A. Schweizer, Sunit Das

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersOntario Ministry of Research and InnovationUniversity of Toronto
KeywordsCognitionComputer scienceBioinformaticsMedicineNeurosciencePsychologyBiology

Abstract

fetched live from OpenAlex

The purpose of the study is to determine driving habits and behaviors of patients with brain tumors in order to better inform discussions around driving safety in this population. Eight-four patients with brain tumors participated in a survey on their driving behaviors since their diagnosis. Thirteen of these patients and thirteen sex- and age-matched healthy controls participated in cognitive testing and several driving simulation scenarios in order to objectively assess driving performance. Survey responses demonstrated that patients with brain tumors engage in a variety of driving scenarios with little subjectve difficulty. On the driving simulation tasks, patients and healthy controls performed similarly except that patients had more speed exceedances (U = 41, p < 0.05) and a greater variability in speed (U = 57, p < 0.05). Performance on the selective attention component of the UFOV was significantly associated with greater total errors in the Bus Following task for patients with brain tumors compared to healthy controls (rs = 0.722, p < 0.05, CI [0.080, 0.957]). Better comprehensive driving assessments are needed to identify patients with driving behaviors that put themselves and others at risk on the road.

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)0.002
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.360
Teacher spread0.341 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicOlder Adults Driving StudiesFrench-language works237,207