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Psychiatric Hospitalization and Veterans With Traumatic Brain Injury

2008· article· en· W2084985657 on OpenAlexaff
Lisa A. Brenner, Jeri E. F. Harwood, Beeta Y. Homaifar, Ellen Cawthra, Jeffrey Waldman, Lawrence E. Adler

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2008
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsAdler
FundersU.S. Public Health Service
KeywordsTraumatic brain injuryPsychiatryMedicinePsychologyMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine risk factors for psychiatric hospitalization after traumatic brain injury (TBI) in veterans. SUBJECTS AND PROCEDURES: Medical records of 96 veterans with histories of TBI (17 mild, 33 moderate, and 46 severe) were reviewed for information concerning psychiatric history, including hospitalization and substance misuse. RESULTS: Subjects with a history of problematic drug and alcohol use had a significantly higher probability of psychiatric hospitalization than those without such a history. Gender, age, problematic alcohol use without problematic drug use, injury severity, time since injury, years of follow-up, and a history of psychiatric symptoms (including those attributed to general medical conditions) were not identified as significant risk factors. Ninety-one veterans (95%) had a history of psychiatric difficulty. In addition, the probability of post-TBI problematic drug and alcohol use, given a pre-TBI history of such use, was significantly higher than the probability given no history. CONCLUSIONS: Veterans with problematic drug and alcohol use are at increased risk for psychiatric hospitalization after TBI. In addition, the likelihood of problematic post-TBI drug and alcohol use was significantly greater for those with a preinjury history. Ninety-five percent of veterans in the current sample endorsed lifetime histories of psychiatric difficulty. These findings highlight the need for evidence-based means of psychiatric and/or substance abuse treatment of those with a history of TBI.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.345
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations36
Published2008
Admission routes1
Has abstractyes

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