MétaCan
Menu
Back to cohort
Record W1989580381 · doi:10.1080/02699050701785054

Brain injury in a forensic psychiatry population

2007· article· en· W1989580381 on OpenAlexafffundabout
Angela Colantonio, Vess Stamenova, C. Abramowitz, Diana E. Clarke, Bruce K. Christensen

Bibliographic record

VenueBrain Injury · 2007
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre for Addiction and Mental HealthToronto Rehabilitation InstituteUniversity of Toronto
FundersOntario Neurotrauma FoundationCentre for Addiction and Mental HealthHamilton Health Sciences
KeywordsPsychiatryMedical recordTraumatic brain injuryPsychiatric historyPopulationMedicinePoison controlMedical diagnosisMedical historySchizophrenia (object-oriented programming)Substance abuseDemographicsInjury preventionForensic psychiatryPsychologyMedical emergencyDemographySurgeryEpilepsy

Abstract

fetched live from OpenAlex

OBJECTIVES: The prevalence and profile of adults with a history of traumatic brain injury (TBI) has not been studied in large North American forensic mental health populations. This study investigated how adults with a documented history of TBI differed with the non-TBI forensic population with respect to demographics, psychiatric diagnoses and history of offences. METHOD: A retrospective chart review of all consecutive admissions to a forensic psychiatry programme in Toronto, Canada was conducted. Information on history of TBI, psychiatric diagnoses, living environments and types of criminal offences were obtained from medical records. RESULTS: History of TBI was ascertained in 23% of 394 eligible patient records. Compared to those without a documented history of TBI, persons with this history were less likely to be diagnosed with schizophrenia but more likely to have alcohol/substance abuse disorder. There were also differences observed with respect to offence profiles. CONCLUSIONS: This study provides evidence to support routine screening for a history of TBI in forensic psychiatry.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.042
GPT teacher head0.372
Teacher spread0.331 · 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

Citations48
Published2007
Admission routes3
Has abstractyes

Explore more

Same venueBrain InjurySame topicTraumatic Brain Injury ResearchFrench-language works237,207