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Record W1975393229 · doi:10.1080/09638280310001639722

Long term outcomes after moderate to severe traumatic brain injury

2004· article· en· W1975393229 on OpenAlexaff
Angela Colantonio, G. Ratcliff, Susan K. Chase, S. F. Kelsey, Michael Escobar, Lee Vernich

Bibliographic record

VenueDisability and Rehabilitation · 2004
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Aging
KeywordsTraumatic brain injuryPhysical medicine and rehabilitationPsychologyMedicineTerm (time)RehabilitationPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: This research examined the long-term outcomes of rehabilitation patients with moderate to severe traumatic brain injury (TBI). DESIGN: Retrospective cohort study. SETTING AND SUBJECTS: We examined consecutive records of persons with moderate to severe traumatic brain injury who were discharged from a large rehabilitation hospital in Pennsylvania from 1973 to 1989. We interviewed consenting participants (n = 306) up to 24 years post-injury. MAIN OUTCOME MEASURES: Self-rated health, activity limitations, employment, living arrangements, marital status, Community Integration Questionnaire, and use of rehabilitation services. RESULTS: Participants were most limited in activities such as managing money and shopping. Twenty-nine per cent of our participants were working full time. There were significant relationships between activity limitations and residual cognitive impairment at follow-up. Self-rated health was correlated with most instrumental activities of daily living. CONCLUSION: Our findings document health and function in a large post acute TBI population and implications for rehabilitation are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.366
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), 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

Citations210
Published2004
Admission routes1
Has abstractyes

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