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Record W2056578391 · doi:10.1097/htr.0b013e31827c7d15

Assessing Aggression Following Traumatic Brain Injury

2013· review· en· W2056578391 on OpenAlexafffund
Michael D. Cusimano, Scott A. Holmes, Carolyn P. Sawicki, Jane Topolovec‐Vranic

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2013
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsAggressionPsychologyRehabilitationNeuropsychologyPopulationTraumatic brain injuryClinical psychologyExternal validityPoison controlPsychiatryMedicineMedical emergencySocial psychologyCognition

Abstract

fetched live from OpenAlex

BACKGROUND: Every year, millions of people worldwide suffer traumatic brain injuries (TBIs). Aggressive behavior, a known psychological symptom following TBI, has been regarded as an obstacle toward rehabilitation. Having measures that accurately assess aggression during rehabilitation is critical toward proper evaluation. OBJECTIVE: To undertake a systematic review of the validated scales used to assess aggression in the postacute stage (≥3 months) after sustaining a TBI in the adult population. A comprehensive search was performed and studies meeting the inclusion criteria were reviewed in full. Quality and validity of supporting articles were assessed via the Downs and Black and QUADAS checklists along with their supporting statistics. RESULTS: A total of 1329 articles were reviewed from the literature. Thirty-two were reviewed in detail and 6 studies eventually passed the exclusion criteria. Of these, 6 neuropsychological scales were represented pertaining to the measurement of aggressive behavior; however, only 1 directly addressed the validity of their scale's aggression component. CONCLUSIONS: Further research is required to establish the validity of scales that specifically address aggression for use in the adult TBI population which could be used to support rehabilitation and social reintegration strategies.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.197
GPT teacher head0.503
Teacher spread0.306 · 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 designOther design
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

Citations13
Published2013
Admission routes2
Has abstractyes

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