MétaCan
Menu
Back to cohort
Record W2108184341 · doi:10.3390/bs4040352

Definition of Impulsivity and Related Terms Following Traumatic Brain Injury: A Review of the Different Concepts and Measures Used to Assess Impulsivity, Disinhibition and other Related Concepts

2014· review· en· W2108184341 on OpenAlexaff
Andrea Kocka, Jean Gagnon

Bibliographic record

VenueBehavioral Sciences · 2014
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsImpulsivityDisinhibitionSensation seekingPsychologyImpulse (physics)Traumatic brain injuryConstruct (python library)SequelaClinical psychologyDevelopmental psychologyCognitive psychologyPersonalityPsychiatrySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Impulsivity is a common and debilitating sequela following traumatic brain injury (TBI), but there is no consensual definition or measure to assess this construct. The following review aims to elucidate the differences and resemblances between impulsivity, disinhibition and other related terms following brain injury and the instruments that are commonly used to measure these constructs. To do so, a search through different databases was conducted in order to find articles that mention and define impulsivity, disinhibition, impulse control, regulation deficits, dyscontrol and risky behavior. The concepts that stand out from the literature, the measures used, the similarities, the differences between these concepts are observed. The fit with the UPPS model of impulsivity, according to which impulsivity is a multidimensional concept composed of four distinct dimensions (urgency, perseverance, premeditation and sensation-seeking) is discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.300
GPT teacher head0.491
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations63
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueBehavioral SciencesSame topicTraumatic Brain Injury ResearchFrench-language works237,207