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Record W2316418405 · doi:10.1097/htr.0000000000000112

A Profile of Patients With Traumatic Brain Injury Within Home Care, Long-Term Care, Complex Continuing Care, and Institutional Mental Health Settings in a Publicly Insured Population

2015· article· en· W2316418405 on OpenAlexafffundabout
Angela Colantonio, Jayden Hsueh, Josian Petgrave, John P. Hirdes, Katherine Berg

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsPublic Health Ontario
FundersCanadian Institutes of Health Research
KeywordsMinimum Data SetMedicineMoodTraumatic brain injuryPopulationMental healthHealth careMood disordersContinuing carePsychiatryLong-term careNursing homesNursingEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the sociodemographic and clinical profile of people with traumatic brain injury (TBI) in home care, nursing homes, and complex continuing care settings in a national sample. METHODS: Cross-sectional study using available Resident Assessment Instrument (RAI 2.0 and RAI Home Care [HC]) national databases in Canada from 1996 to 2011. The profile of people with TBI was compared with patients with and without prespecified neurological conditions within each setting. PARTICIPANTS: Adults 18 years and older identified with TBI (n = 10 878) and adult patients with other neurological (n = 422 300) and non-neurological (n = 571 567) conditions. MAIN MEASURES: Demographic and clinical characteristics, functional characteristics, mood and behavior, and treatment and medication variables. Data from Canadian home care (RAI-HC), mental health (RAI-MH), nursing home, and complex continuing care facilities (RAI Minimum Data Set 2.0). RESULTS: Patients with TBI were significantly different on almost all items. They were among the youngest in care settings, and psychotropic drug use by this population was among the highest in at least 2 settings. CONCLUSION: These data can inform the planning for appropriate care and resources for patients with TBI in a range of settings.

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 score0.135
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.350
Teacher spread0.313 · 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

Citations19
Published2015
Admission routes3
Has abstractyes

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