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Record W2078823477 · doi:10.1080/02699050802372166

An overview of traumatic brain injury and suicide

2008· review· en· W2078823477 on OpenAlexaff
Lori Drucker Wasserman, Tammy Shaw, Michael T. Vu, Clara Ko, Dimitri Bollegala, Shree Bhalerao

Bibliographic record

VenueBrain Injury · 2008
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's HospitalYork UniversityUniversity of Toronto
Fundersnot available
KeywordsTraumatic brain injuryPsychosocialPsychoeducationPsychiatrySuicide preventionBiopsychosocial modelMedicinePoison controlConcussionDepression (economics)Injury preventionPopulationPsychologyClinical psychologyPsychological interventionMedical emergency

Abstract

fetched live from OpenAlex

PURPOSE: There is concerning evidence that people with traumatic brain injury (TBI) may be at increased risk for suicide. This paper aims to provide an overview of traumatic brain injury and suicide in order to enhance the ability of professionals to recognize and manage suicidality in patients with TBI. METHODS: First, the association between TBI and suicide is reviewed. Proposed psychological, psychosocial and neuropathological factors are included in the discussion. Next, identifiable risk factors for suicide in TBI are presented. Suicide assessment tools are then discussed. Assessment is emphasized as the mainstay of suicide prevention and clinicians are encouraged to be vigilant for potential suicidality in their patients with TBI. Finally, biopsychosocial interventions for suicidality are reviewed. CONCLUSIONS: This paper concludes that increasing awareness of depression and suicide risk assessment in the TBI population should be aimed towards staff involved in neuro-rehabilation as well as other professionals who are involved in the care of patients with TBI, because psychoeducation of those most likely to come in contact with at-risk individuals have been shown to increase identification of suicidal patients, lowering suicide rates.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.899
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.275
GPT teacher head0.477
Teacher spread0.202 · 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 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

Citations72
Published2008
Admission routes1
Has abstractyes

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